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MastersThesis/thesis_output/plantilla_individual.htm

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2025-12-15 23:54:41 +01:00
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span.MsoFootnoteReference
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margin-bottom:6.0pt;
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a:link, span.MsoHyperlink
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color:#0563C1;
text-decoration:underline;
text-underline:single;}
a:visited, span.MsoHyperlinkFollowed
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color:#954F72;
mso-themecolor:followedhyperlink;
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mso-ansi-language:X-NONE;
mso-fareast-language:ES;}
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mso-style-unhide:no;
mso-style-parent:"";
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mso-ansi-language:ES;
mso-fareast-language:EN-US;}
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mso-style-unhide:no;
mso-style-qformat:yes;
margin-top:6.0pt;
margin-right:0cm;
margin-bottom:6.0pt;
margin-left:36.0pt;
mso-add-space:auto;
text-align:justify;
line-height:150%;
mso-pagination:widow-orphan;
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font-family:"Calibri",sans-serif;
mso-ascii-font-family:Calibri;
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mso-ansi-language:ES;
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p.MsoListParagraphCxSpFirst, li.MsoListParagraphCxSpFirst, div.MsoListParagraphCxSpFirst
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mso-style-type:export-only;
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margin-right:0cm;
margin-bottom:0cm;
margin-left:36.0pt;
mso-add-space:auto;
text-align:justify;
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font-family:"Calibri",sans-serif;
mso-ascii-font-family:Calibri;
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mso-bidi-font-family:"Times New Roman";
mso-ansi-language:ES;
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p.MsoListParagraphCxSpMiddle, li.MsoListParagraphCxSpMiddle, div.MsoListParagraphCxSpMiddle
{mso-style-priority:34;
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mso-style-qformat:yes;
mso-style-type:export-only;
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margin-right:0cm;
margin-bottom:0cm;
margin-left:36.0pt;
mso-add-space:auto;
text-align:justify;
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font-family:"Calibri",sans-serif;
mso-ascii-font-family:Calibri;
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mso-hansi-font-family:Calibri;
mso-hansi-theme-font:minor-latin;
mso-bidi-font-family:"Times New Roman";
mso-ansi-language:ES;
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p.MsoListParagraphCxSpLast, li.MsoListParagraphCxSpLast, div.MsoListParagraphCxSpLast
{mso-style-priority:34;
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margin-right:0cm;
margin-bottom:6.0pt;
margin-left:36.0pt;
mso-add-space:auto;
text-align:justify;
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font-family:"Calibri",sans-serif;
mso-ascii-font-family:Calibri;
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mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Calibri;
mso-hansi-theme-font:minor-latin;
mso-bidi-font-family:"Times New Roman";
mso-ansi-language:ES;
mso-fareast-language:ES;}
p.MsoQuote, li.MsoQuote, div.MsoQuote
{mso-style-priority:29;
mso-style-unhide:no;
mso-style-qformat:yes;
mso-style-link:"Quote Char";
mso-style-next:Normal;
margin-top:6.0pt;
margin-right:0cm;
margin-bottom:6.0pt;
margin-left:0cm;
text-align:justify;
mso-pagination:widow-orphan;
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font-family:"Calibri",sans-serif;
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mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Calibri;
mso-hansi-theme-font:minor-latin;
mso-bidi-font-family:"Times New Roman";
color:#404040;
mso-ansi-language:ES;
mso-fareast-language:ES;
mso-bidi-font-style:italic;}
p.MsoBibliography, li.MsoBibliography, div.MsoBibliography
{mso-style-noshow:yes;
mso-style-priority:37;
mso-style-next:Normal;
margin-top:6.0pt;
margin-right:0cm;
margin-bottom:6.0pt;
margin-left:0cm;
text-align:justify;
line-height:150%;
mso-pagination:widow-orphan;
font-size:12.0pt;
font-family:"Calibri",sans-serif;
mso-ascii-font-family:Calibri;
mso-ascii-theme-font:minor-latin;
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Calibri;
mso-hansi-theme-font:minor-latin;
mso-bidi-font-family:"Times New Roman";
mso-ansi-language:ES;
mso-fareast-language:ES;}
p.MsoTocHeading, li.MsoTocHeading, div.MsoTocHeading
{mso-style-name:"TOC Heading\,Tablas";
mso-style-priority:39;
mso-style-next:Normal;
margin-top:6.0pt;
margin-right:0cm;
margin-bottom:6.0pt;
margin-left:0cm;
text-align:justify;
line-height:150%;
mso-pagination:widow-orphan;
tab-stops:right dotted 453.0pt;
font-size:12.0pt;
font-family:"Calibri",sans-serif;
mso-ascii-font-family:Calibri;
mso-ascii-theme-font:minor-latin;
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Calibri;
mso-hansi-theme-font:minor-latin;
mso-bidi-font-family:Arial;
mso-ansi-language:ES;
mso-fareast-language:ES;
mso-no-proof:yes;}
span.NoSpacingChar
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mso-style-priority:1;
mso-style-unhide:no;
mso-style-locked:yes;
mso-style-parent:"";
mso-style-link:"No Spacing";
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mso-bidi-font-size:11.0pt;
font-family:"Times New Roman",serif;
mso-fareast-font-family:"Times New Roman";
mso-ansi-language:ES;
mso-fareast-language:EN-US;
mso-bidi-language:AR-SA;}
span.BalloonTextChar
{mso-style-name:"Balloon Text Char";
mso-style-noshow:yes;
mso-style-priority:99;
mso-style-unhide:no;
mso-style-locked:yes;
mso-style-parent:"";
mso-style-link:"Balloon Text";
mso-ansi-font-size:8.0pt;
mso-bidi-font-size:8.0pt;
font-family:"Tahoma",sans-serif;
mso-ascii-font-family:Tahoma;
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Tahoma;
mso-bidi-font-family:Tahoma;
mso-fareast-language:ES;}
span.HeaderChar
{mso-style-name:"Header Char";
mso-style-priority:99;
mso-style-unhide:no;
mso-style-locked:yes;
mso-style-parent:"";
mso-style-link:Header;
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mso-bidi-font-size:12.0pt;
font-family:"Times New Roman",serif;
mso-ascii-font-family:"Times New Roman";
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:"Times New Roman";}
span.FooterChar
{mso-style-name:"Footer Char";
mso-style-priority:99;
mso-style-unhide:no;
mso-style-locked:yes;
mso-style-parent:"";
mso-style-link:Footer;
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mso-bidi-font-size:12.0pt;
font-family:"Times New Roman",serif;
mso-ascii-font-family:"Times New Roman";
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:"Times New Roman";}
span.Heading1Char
{mso-style-name:"Heading 1 Char";
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mso-hansi-font-family:"Calibri Light";
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color:#0098CD;
text-transform:uppercase;
mso-font-kerning:16.0pt;
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span.Heading2Char
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font-family:"Calibri Light",sans-serif;
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mso-bidi-font-family:Arial;
color:#0098CD;
text-transform:uppercase;
mso-bidi-font-weight:bold;
mso-bidi-font-style:italic;}
span.Heading3Char
{mso-style-name:"Heading 3 Char";
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mso-style-parent:"";
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mso-ascii-font-family:"Calibri Light";
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mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:"Calibri Light";
mso-hansi-theme-font:major-latin;
font-weight:bold;}
span.TitleChar
{mso-style-name:"Title Char\,Subtitulos 3er nivel Char";
mso-style-priority:10;
mso-style-unhide:no;
mso-style-locked:yes;
mso-style-parent:"";
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font-family:"Arial",sans-serif;
mso-ascii-font-family:Arial;
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mso-bidi-font-family:"Times New Roman";
mso-font-kerning:14.0pt;
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font-style:italic;
mso-bidi-font-style:normal;}
span.Heading4Char
{mso-style-name:"Heading 4 Char";
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mso-style-parent:"";
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font-family:"Calibri",sans-serif;
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mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Calibri;
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span.QuoteChar
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mso-style-link:Quote;
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mso-ascii-font-family:Arial;
mso-fareast-font-family:"Times New Roman";
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color:#404040;
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span.Heading5Char
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mso-style-link:"Heading 5";
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mso-ascii-font-family:Calibri;
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Calibri;
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font-weight:bold;
font-style:italic;}
span.Heading6Char
{mso-style-name:"Heading 6 Char";
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mso-ascii-font-family:Calibri;
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Calibri;
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font-weight:bold;}
span.Heading7Char
{mso-style-name:"Heading 7 Char";
mso-style-noshow:yes;
mso-style-priority:9;
mso-style-unhide:no;
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mso-style-link:"Heading 7";
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mso-ascii-font-family:Calibri;
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Calibri;
mso-bidi-font-family:"Times New Roman";}
span.Heading8Char
{mso-style-name:"Heading 8 Char";
mso-style-noshow:yes;
mso-style-priority:9;
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mso-style-locked:yes;
mso-style-parent:"";
mso-style-link:"Heading 8";
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mso-ascii-font-family:Calibri;
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Calibri;
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font-style:italic;}
span.Heading9Char
{mso-style-name:"Heading 9 Char";
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font-family:"Calibri Light",sans-serif;
mso-ascii-font-family:"Calibri Light";
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:"Calibri Light";
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span.CommentTextChar
{mso-style-name:"Comment Text Char";
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font-family:"Arial",sans-serif;
mso-ascii-font-family:Arial;
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Arial;}
span.CommentSubjectChar
{mso-style-name:"Comment Subject Char";
mso-style-noshow:yes;
mso-style-priority:99;
mso-style-unhide:no;
mso-style-locked:yes;
mso-style-parent:"";
mso-style-link:"Comment Subject";
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mso-ascii-font-family:Arial;
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Arial;
font-weight:bold;}
p.Figuras, li.Figuras, div.Figuras
{mso-style-name:Figuras;
mso-style-update:auto;
mso-style-unhide:no;
mso-style-link:"Figuras Car";
margin-top:6.0pt;
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margin-bottom:6.0pt;
margin-left:0cm;
text-align:center;
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mso-pagination:widow-orphan;
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mso-ascii-font-family:Calibri;
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mso-hansi-font-family:Calibri;
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mso-ansi-language:ES;
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span.FigurasCar
{mso-style-name:"Figuras Car";
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mso-style-parent:"";
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p.PiedepginaSecciones, li.PiedepginaSecciones, div.PiedepginaSecciones
{mso-style-name:"Pie de página_Secciones";
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margin-top:6.0pt;
margin-right:0cm;
margin-bottom:5.0pt;
margin-left:0cm;
text-align:right;
mso-pagination:widow-orphan;
tab-stops:right 411.0pt;
font-size:10.0pt;
font-family:"Calibri",sans-serif;
mso-fareast-font-family:"Times New Roman";
mso-bidi-font-family:UnitOT-Light;
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mso-ansi-language:ES;
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mso-bidi-font-weight:bold;
mso-no-proof:yes;}
p.Ttulondices, li.Ttulondices, div.Ttulondices
{mso-style-name:"Título Índices";
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mso-style-qformat:yes;
mso-style-link:"Título Índices Car";
margin-top:6.0pt;
margin-right:0cm;
margin-bottom:6.0pt;
margin-left:0cm;
text-align:justify;
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mso-pagination:widow-orphan;
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mso-hansi-theme-font:major-latin;
mso-bidi-font-family:Arial;
color:#0098CD;
mso-ansi-language:ES;
mso-fareast-language:EN-US;}
p.Pgina, li.Pgina, div.Pgina
{mso-style-name:Página;
mso-style-unhide:no;
mso-style-qformat:yes;
mso-style-parent:Footer;
mso-style-link:"Página Car";
margin-top:6.0pt;
margin-right:0cm;
margin-bottom:6.0pt;
margin-left:0cm;
text-align:right;
line-height:150%;
mso-pagination:widow-orphan;
tab-stops:center 212.6pt right 425.2pt;
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mso-bidi-font-size:11.0pt;
font-family:"Calibri",sans-serif;
mso-ascii-font-family:Calibri;
mso-ascii-theme-font:minor-latin;
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Calibri;
mso-hansi-theme-font:minor-latin;
mso-bidi-font-family:Arial;
mso-ansi-language:ES;
mso-fareast-language:ES;}
span.TtulondicesCar
{mso-style-name:"Título Índices Car";
mso-style-unhide:no;
mso-style-locked:yes;
mso-style-link:"Título Índices";
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mso-bidi-font-size:18.0pt;
font-family:"Calibri Light",sans-serif;
mso-ascii-font-family:"Calibri Light";
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mso-hansi-theme-font:major-latin;
mso-bidi-font-family:Arial;
color:#0098CD;
mso-fareast-language:EN-US;}
span.PginaCar
{mso-style-name:"Página Car";
mso-style-unhide:no;
mso-style-locked:yes;
mso-style-parent:"Footer Char";
mso-style-link:Página;
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mso-bidi-font-size:11.0pt;
font-family:"Calibri",sans-serif;
mso-ascii-font-family:Calibri;
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mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Calibri;
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mso-bidi-font-family:Arial;}
span.FootnoteTextChar
{mso-style-name:"Footnote Text Char";
mso-style-unhide:no;
mso-style-locked:yes;
mso-style-link:"Footnote Text";
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mso-ascii-font-family:Calibri;
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mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:Calibri;
mso-hansi-theme-font:minor-latin;}
p.Ttulo1sinnumerar, li.Ttulo1sinnumerar, div.Ttulo1sinnumerar
{mso-style-name:"Título 1 sin numerar";
mso-style-unhide:no;
mso-style-qformat:yes;
mso-style-parent:"Heading 1";
margin-top:6.0pt;
margin-right:0cm;
margin-bottom:6.0pt;
margin-left:0cm;
text-align:justify;
line-height:150%;
mso-pagination:widow-orphan;
page-break-after:avoid;
mso-outline-level:1;
font-size:18.0pt;
mso-bidi-font-size:16.0pt;
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mso-ascii-font-family:"Calibri Light";
mso-ascii-theme-font:major-latin;
mso-fareast-font-family:"Times New Roman";
mso-hansi-font-family:"Calibri Light";
mso-hansi-theme-font:major-latin;
mso-bidi-font-family:"Times New Roman";
color:#0098CD;
mso-font-kerning:16.0pt;
mso-ansi-language:ES;
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<body link="#0563C1" style="tab-interval:35.4pt;word-wrap:break-word" vlink="#954F72">
<div class="WordSection1">
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<p align="center" class="MsoNoSpacing" style="text-align:center;line-height:115%"><span lang="ES" style='font-size:24.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
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Descripción generada automáticamente" height="91" src="plantilla_individual_files/image002.gif" v:shapes="Imagen_x0020_5126" width="403"/><?endif?></span><span lang="ES" style='font-size:24.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:"Open Sans"'><o:p></o:p></span></p>
<p align="center" class="MsoNoSpacing" style="text-align:center;line-height:115%"><span lang="ES" style='font-size:24.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:"Open Sans"'>Universidad
Internacional de La Rioja<o:p></o:p></span></p>
<p align="center" class="MsoNoSpacing" style="text-align:center;line-height:115%"><span lang="ES" style='font-size:20.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:"Open Sans"'>Escuela
Superior de Ingeniería y<o:p></o:p></span></p>
<p align="center" class="MsoNoSpacing" style="text-align:center;line-height:115%"><span lang="ES" style='font-size:20.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:"Open Sans"'>Tecnología<o:p></o:p></span></p>
<p align="center" class="MsoNoSpacing" style="text-align:center;line-height:115%"><span lang="ES" style="font-size:20.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
mso-ascii-font-family:Calibri;mso-ascii-theme-font:minor-latin;mso-hansi-font-family:
Calibri;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Tahoma"><o:p> </o:p></span></p>
<p align="center" class="MsoNoSpacing" style="text-align:center;line-height:115%"><span lang="ES" style="font-size:20.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
mso-ascii-font-family:Calibri;mso-ascii-theme-font:minor-latin;mso-hansi-font-family:
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<p align="center" class="MsoNoSpacing" style="text-align:center;line-height:115%"><span lang="ES" style="font-size:20.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
mso-ascii-font-family:Calibri;mso-ascii-theme-font:minor-latin;mso-hansi-font-family:
Calibri;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Tahoma"><o:p> </o:p></span></p>
<p align="center" class="MsoNoSpacing" style="text-align:center;line-height:115%"><span lang="ES" style="font-size:20.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
mso-ascii-font-family:Calibri;mso-ascii-theme-font:minor-latin;mso-hansi-font-family:
Calibri;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Tahoma"><o:p> </o:p></span></p>
<p align="center" class="MsoNoSpacing" style="text-align:center;line-height:115%"><span lang="ES" style='font-size:18.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:Tahoma'>Máster Universitario
en Inteligencia artificial</span><span lang="ES" style="font-size:26.0pt;
mso-bidi-font-size:14.0pt;line-height:115%;mso-ascii-font-family:Calibri;
mso-ascii-theme-font:minor-latin;mso-hansi-font-family:Calibri;mso-hansi-theme-font:
minor-latin;mso-bidi-font-family:Tahoma;color:#0098CD"><o:p></o:p></span></p>
<p class="MsoNormal"><a name="_Hlk216726907"><span lang="ES" style="font-size:26.0pt;
mso-bidi-font-size:14.0pt;line-height:150%;mso-bidi-font-family:Tahoma;
color:#0098CD;mso-fareast-language:EN-US">Optimización de Hiperparámetros OCR
con Ray Tune para Documentos Académicos en </span></a><span class="GramE"><span style="mso-bookmark:_Hlk216726907"><span lang="ES" style="font-size:26.0pt;
mso-bidi-font-size:14.0pt;line-height:150%;mso-bidi-font-family:Tahoma;
color:#0098CD;mso-fareast-language:EN-US">Español</span></span></span><span style="mso-bookmark:_Hlk216726907"><span lang="ES" style="mso-fareast-language:
EN-US"><o:p></o:p></span></span></p>
<span style="mso-bookmark:_Hlk216726907"></span>
<p class="MsoNormal"><span lang="ES" style="mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="ES" style="mso-fareast-language:EN-US"><span style="mso-tab-count:1">            </span><o:p></o:p></span></p>
<table align="left" border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid #E7E6E6 .5pt;
mso-border-themecolor:background2;mso-yfti-tbllook:1184;mso-table-lspace:7.05pt;
margin-left:4.8pt;mso-table-rspace:7.05pt;margin-right:4.8pt;mso-table-anchor-vertical:
page;mso-table-anchor-horizontal:margin;mso-table-left:left;mso-table-top:
609.8pt;mso-padding-alt:0cm 5.4pt 0cm 5.4pt;mso-border-insideh:.5pt solid #E7E6E6;
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<tr style="mso-yfti-irow:0;mso-yfti-firstrow:yes">
<td style="width:198.2pt;border:solid #E7E6E6 1.0pt;mso-border-themecolor:
background2;mso-border-alt:solid #E7E6E6 .5pt;mso-border-themecolor:background2;
padding:0cm 5.4pt 0cm 5.4pt" width="264">
<p class="MsoNoSpacing" style="line-height:115%;mso-element:frame;mso-element-frame-hspace:
7.05pt;mso-element-wrap:around;mso-element-anchor-vertical:page;mso-element-anchor-horizontal:
margin;mso-element-top:609.8pt;mso-height-rule:exactly"><span lang="ES" style='font-size:12.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:Tahoma'>Trabajo fin de
estudio presentado por:<o:p></o:p></span></p>
</td>
<td style="width:227.45pt;border:solid #E7E6E6 1.0pt;mso-border-themecolor:
background2;border-left:none;mso-border-left-alt:solid #E7E6E6 .5pt;
mso-border-left-themecolor:background2;mso-border-alt:solid #E7E6E6 .5pt;
mso-border-themecolor:background2;padding:0cm 5.4pt 0cm 5.4pt" width="303">
<p class="MsoNoSpacing" style="line-height:115%;mso-element:frame;mso-element-frame-hspace:
7.05pt;mso-element-wrap:around;mso-element-anchor-vertical:page;mso-element-anchor-horizontal:
margin;mso-element-top:609.8pt;mso-height-rule:exactly"><span lang="ES" style='font-size:12.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:Tahoma'>Sergio Jiménez <span class="SpellE">Jiménez</span><o:p></o:p></span></p>
</td>
</tr>
<tr style="mso-yfti-irow:1">
<td style="width:198.2pt;border:solid #E7E6E6 1.0pt;mso-border-themecolor:
background2;border-top:none;mso-border-top-alt:solid #E7E6E6 .5pt;mso-border-top-themecolor:
background2;mso-border-alt:solid #E7E6E6 .5pt;mso-border-themecolor:background2;
padding:0cm 5.4pt 0cm 5.4pt" width="264">
<p class="MsoNoSpacing" style="line-height:115%;mso-element:frame;mso-element-frame-hspace:
7.05pt;mso-element-wrap:around;mso-element-anchor-vertical:page;mso-element-anchor-horizontal:
margin;mso-element-top:609.8pt;mso-height-rule:exactly"><span lang="ES" style='font-size:12.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:Tahoma'>Tipo de
trabajo:<o:p></o:p></span></p>
</td>
<td style="width:227.45pt;border-top:none;border-left:none;
border-bottom:solid #E7E6E6 1.0pt;mso-border-bottom-themecolor:background2;
border-right:solid #E7E6E6 1.0pt;mso-border-right-themecolor:background2;
mso-border-top-alt:solid #E7E6E6 .5pt;mso-border-top-themecolor:background2;
mso-border-left-alt:solid #E7E6E6 .5pt;mso-border-left-themecolor:background2;
mso-border-alt:solid #E7E6E6 .5pt;mso-border-themecolor:background2;
padding:0cm 5.4pt 0cm 5.4pt" width="303">
<p class="MsoNoSpacing" style="line-height:115%;mso-element:frame;mso-element-frame-hspace:
7.05pt;mso-element-wrap:around;mso-element-anchor-vertical:page;mso-element-anchor-horizontal:
margin;mso-element-top:609.8pt;mso-height-rule:exactly"><span lang="ES" style='font-size:12.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:Tahoma'>Desarrollo
Software<o:p></o:p></span></p>
</td>
</tr>
<tr style="mso-yfti-irow:2">
<td style="width:198.2pt;border:solid #E7E6E6 1.0pt;mso-border-themecolor:
background2;border-top:none;mso-border-top-alt:solid #E7E6E6 .5pt;mso-border-top-themecolor:
background2;mso-border-alt:solid #E7E6E6 .5pt;mso-border-themecolor:background2;
padding:0cm 5.4pt 0cm 5.4pt" width="264">
<p class="MsoNoSpacing" style="line-height:115%;mso-element:frame;mso-element-frame-hspace:
7.05pt;mso-element-wrap:around;mso-element-anchor-vertical:page;mso-element-anchor-horizontal:
margin;mso-element-top:609.8pt;mso-height-rule:exactly"><span lang="ES" style='font-size:12.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:Tahoma'>Director/a:<o:p></o:p></span></p>
</td>
<td style="width:227.45pt;border-top:none;border-left:none;
border-bottom:solid #E7E6E6 1.0pt;mso-border-bottom-themecolor:background2;
border-right:solid #E7E6E6 1.0pt;mso-border-right-themecolor:background2;
mso-border-top-alt:solid #E7E6E6 .5pt;mso-border-top-themecolor:background2;
mso-border-left-alt:solid #E7E6E6 .5pt;mso-border-left-themecolor:background2;
mso-border-alt:solid #E7E6E6 .5pt;mso-border-themecolor:background2;
padding:0cm 5.4pt 0cm 5.4pt" width="303">
<p class="MsoNoSpacing" style="line-height:115%;mso-element:frame;mso-element-frame-hspace:
7.05pt;mso-element-wrap:around;mso-element-anchor-vertical:page;mso-element-anchor-horizontal:
margin;mso-element-top:609.8pt;mso-height-rule:exactly"><span lang="ES" style='font-size:12.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:Tahoma'>Javier Rodrigo
Villazón Terrazas<o:p></o:p></span></p>
</td>
</tr>
<tr style="mso-yfti-irow:3;mso-yfti-lastrow:yes">
<td style="width:198.2pt;border:solid #E7E6E6 1.0pt;mso-border-themecolor:
background2;border-top:none;mso-border-top-alt:solid #E7E6E6 .5pt;mso-border-top-themecolor:
background2;mso-border-alt:solid #E7E6E6 .5pt;mso-border-themecolor:background2;
padding:0cm 5.4pt 0cm 5.4pt" width="264">
<p class="MsoNoSpacing" style="line-height:115%;mso-element:frame;mso-element-frame-hspace:
7.05pt;mso-element-wrap:around;mso-element-anchor-vertical:page;mso-element-anchor-horizontal:
margin;mso-element-top:609.8pt;mso-height-rule:exactly"><span lang="ES" style='font-size:12.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:Tahoma'>Fecha:<o:p></o:p></span></p>
</td>
<td style="width:227.45pt;border-top:none;border-left:none;
border-bottom:solid #E7E6E6 1.0pt;mso-border-bottom-themecolor:background2;
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mso-border-alt:solid #E7E6E6 .5pt;mso-border-themecolor:background2;
padding:0cm 5.4pt 0cm 5.4pt" width="303">
<p class="MsoNoSpacing" style="line-height:115%;mso-element:frame;mso-element-frame-hspace:
7.05pt;mso-element-wrap:around;mso-element-anchor-vertical:page;mso-element-anchor-horizontal:
margin;mso-element-top:609.8pt;mso-height-rule:exactly"><span lang="ES" style='font-size:12.0pt;mso-bidi-font-size:14.0pt;line-height:115%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:Tahoma'>06.10.2025<o:p></o:p></span></p>
</td>
</tr>
</table>
<p class="MsoNormal"><span lang="ES" style="mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<span lang="ES" style='font-size:18.0pt;mso-bidi-font-size:11.0pt;line-height:
150%;font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:major-latin;
mso-bidi-font-family:Arial;color:#0098CD;mso-ansi-language:ES;mso-fareast-language:
EN-US;mso-bidi-language:AR-SA'><br clear="all" style="page-break-before:always"/>
</span>
<p class="Ttulondices"><span lang="ES">Resumen </span></p><p class="MsoNormal"><span lang="ES">El presente Trabajo Fin de Máster aborda la optimización de sistemas de Reconocimiento Óptico de Caracteres (OCR) basados en inteligencia artificial para documentos en español, específicamente en un entorno con recursos computacionales limitados donde el fine-tuning de modelos no es viable. El objetivo principal es identificar la configuración óptima de hiperparámetros que maximice la precisión del reconocimiento de texto sin requerir entrenamiento adicional de los modelos.
Se realizó un estudio comparativo de tres soluciones OCR de código abierto: EasyOCR, PaddleOCR (PP-OCRv5) y DocTR, evaluando su rendimiento mediante las métricas estándar CER (Character Error Rate) y WER (Word Error Rate) sobre un corpus de documentos académicos en español. Tras identificar PaddleOCR como la solución más prometedora, se procedió a una optimización sistemática de hiperparámetros utilizando Ray Tune con el algoritmo de búsqueda Optuna, ejecutando 64 configuraciones diferentes.
Los resultados demuestran que la optimización de hiperparámetros logró una mejora significativa del rendimiento: el CER se redujo de 7.78% a 1.49% (mejora del 80.9% en reducción de errores), alcanzando una precisión de caracteres del 98.51%. El hallazgo más relevante fue que el parámetro `textline_orientation` (clasificación de orientación de línea de texto) tiene un impacto crítico, reduciendo el CER en un 69.7% cuando está habilitado. Adicionalmente, se identificó que el umbral de detección de píxeles (`text_det_thresh`) presenta una correlación negativa fuerte (-0.52) con el error, siendo el parámetro continuo más influyente.
Este trabajo demuestra que es posible obtener mejoras sustanciales en sistemas OCR mediante optimización de hiperparámetros, ofreciendo una alternativa práctica al fine-tuning cuando los recursos computacionales son limitados.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="ES">Palabras clave:</span></b><span lang="ES"> OCR, Reconocimiento Óptico de Caracteres, PaddleOCR, Optimización de Hiperparámetros, Ray Tune, Procesamiento de Documentos, Inteligencia Artificial</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<p class="Ttulondices"><span style="mso-bookmark:_Toc437509152"><span class="SpellE"><span lang="ES">Abstract</span></span></span></p><p class="MsoNormal"><span lang="EN-US">This Master's Thesis addresses the optimization of Artificial Intelligence-based Optical Character Recognition (OCR) systems for Spanish documents, specifically in a resource-constrained environment where model fine-tuning is not feasible. The main objective is to identify the optimal hyperparameter configuration that maximizes text recognition accuracy without requiring additional model training.
A comparative study of three open-source OCR solutions was conducted: EasyOCR, PaddleOCR (PP-OCRv5), and DocTR, evaluating their performance using standard CER (Character Error Rate) and WER (Word Error Rate) metrics on a corpus of academic documents in Spanish. After identifying PaddleOCR as the most promising solution, systematic hyperparameter optimization was performed using Ray Tune with the Optuna search algorithm, executing 64 different configurations.
Results demonstrate that hyperparameter optimization achieved significant performance improvement: CER was reduced from 7.78% to 1.49% (80.9% error reduction), achieving 98.51% character accuracy. The most relevant finding was that the `textline_orientation` parameter (text line orientation classification) has a critical impact, reducing CER by 69.7% when enabled. Additionally, the pixel detection threshold (`text_det_thresh`) was found to have a strong negative correlation (-0.52) with error, being the most influential continuous parameter.
This work demonstrates that substantial improvements in OCR systems can be obtained through hyperparameter optimization, offering a practical alternative to fine-tuning when computational resources are limited.</span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US">Keywords:</span></b><span lang="EN-US"> OCR, Optical Character Recognition, PaddleOCR, Hyperparameter Optimization, Ray Tune, Document Processing, Artificial Intelligence</span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<span lang="ES" style='font-size:12.0pt;line-height:150%;font-family:"Calibri",sans-serif;
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mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";
mso-ansi-language:ES;mso-fareast-language:ES;mso-bidi-language:AR-SA'><br clear="all" style="mso-special-character:line-break;page-break-before:always"/>
</span>
<p class="MsoNormal"><span style="mso-bookmark:_Toc437509152"><span lang="ES"><o:p> </o:p></span></span></p>
<span style="mso-bookmark:_Toc437509152"></span>
<p class="Ttulondices"><span lang="ES">Índice de contenidos</span></p>
<p class="MsoToc1" style="tab-stops:22.0pt right dotted 453.05pt"><!--[if supportFields]><span
lang=ES style='font-size:11.0pt;mso-bidi-font-size:12.0pt;line-height:150%'><span
style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>TOC \o &quot;1-3&quot; \h \z \u <span
style='mso-element:field-separator'></span></span><![endif]--><span lang="ES"><a href="#_Toc160619805"><span style="mso-no-proof:yes">1.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">    </span></span><span style="mso-no-proof:yes">Introducción</span><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619805 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">1<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800300035000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc2" style="tab-stops:48.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619806"><span style="mso-no-proof:yes">1.1.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">      </span></span><span style="mso-no-proof:yes">Motivación</span><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619806 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">1<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800300036000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc2" style="tab-stops:48.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619807"><span style="mso-no-proof:yes">1.2.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">      </span></span><span style="mso-no-proof:yes">Planteamiento
del trabajo</span><span style="color:windowtext;display:none;mso-hide:screen;
mso-no-proof:yes;text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619807 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">3<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800300037000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc2" style="tab-stops:48.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619808"><span style="mso-no-proof:yes">1.3.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">      </span></span><span style="mso-no-proof:yes">Estructura
del trabajo</span><span style="color:windowtext;display:none;mso-hide:screen;
mso-no-proof:yes;text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619808 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">3<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800300038000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc1" style="tab-stops:22.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619809"><span style="mso-no-proof:yes">2.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">    </span></span><span style="mso-no-proof:yes">Contexto
y estado del arte</span><span style="color:windowtext;display:none;mso-hide:
screen;mso-no-proof:yes;text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619809 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">4<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800300039000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc2" style="tab-stops:48.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619810"><span style="mso-no-proof:yes">2.1.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">      </span></span><span style="mso-no-proof:yes">Contexto
del problema</span><span style="color:windowtext;display:none;mso-hide:screen;
mso-no-proof:yes;text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619810 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">4<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800310030000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc2" style="tab-stops:48.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619811"><span style="mso-no-proof:yes">2.2.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">      </span></span><span style="mso-no-proof:yes">Estado
del arte</span><span style="color:windowtext;display:none;mso-hide:screen;
mso-no-proof:yes;text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619811 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">4<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800310031000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc2" style="tab-stops:48.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619812"><span style="mso-no-proof:yes">2.3.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">      </span></span><span style="mso-no-proof:yes">Conclusiones</span><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619812 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">5<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800310032000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc1" style="tab-stops:22.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619813"><span style="mso-no-proof:yes">3.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">    </span></span><span style="mso-no-proof:yes">Objetivos
concretos y metodología de trabajo</span><span style="color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619813 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">6<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800310033000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc2" style="tab-stops:48.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619814"><span style="mso-no-proof:yes">3.1.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">      </span></span><span style="mso-no-proof:yes">Objetivo
general</span><span style="color:windowtext;display:none;mso-hide:screen;
mso-no-proof:yes;text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted"> </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619814 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">6<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800310034000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc2" style="tab-stops:48.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619815"><span style="mso-no-proof:yes">3.2.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">      </span></span><span style="mso-no-proof:yes">Objetivos
específicos</span><span style="color:windowtext;display:none;mso-hide:screen;
mso-no-proof:yes;text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619815 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">7<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800310035000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc2" style="tab-stops:48.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619816"><span style="mso-no-proof:yes">3.3.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">      </span></span><span style="mso-no-proof:yes">Metodología
del trabajo</span><span style="color:windowtext;display:none;mso-hide:screen;
mso-no-proof:yes;text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619816 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">8<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800310036000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc1" style="tab-stops:22.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619817"><span style='font-family:"Calibri Light",sans-serif;
mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin;mso-bidi-font-family:
"Times New Roman";mso-font-kerning:16.0pt;mso-bidi-font-weight:bold;mso-no-proof:
yes'>4.</span><span style='mso-fareast-font-family:"Times New Roman";
mso-fareast-theme-font:minor-fareast;mso-bidi-font-family:"Times New Roman";
mso-bidi-theme-font:minor-bidi;color:windowtext;mso-font-kerning:1.0pt;
mso-ligatures:standardcontextual;mso-no-proof:yes;text-decoration:none;
text-underline:none'><span style="mso-tab-count:1">    </span></span><span style='font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:"Times New Roman";
text-transform:uppercase;mso-font-kerning:16.0pt;mso-bidi-font-weight:bold;
mso-no-proof:yes'>D</span><span style='font-family:"Calibri Light",sans-serif;
mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin;mso-bidi-font-family:
"Times New Roman";mso-font-kerning:16.0pt;mso-bidi-font-weight:bold;mso-no-proof:
yes'>esarrollo específico de la contribución</span><span style="color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619817 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">9<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800310037000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc1" style="tab-stops:22.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619818"><span style="mso-no-proof:yes">5.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">    </span></span><span style="mso-no-proof:yes">Conclusiones
y trabajo futuro</span><span style="color:windowtext;display:none;mso-hide:
screen;mso-no-proof:yes;text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619818 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">13<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800310038000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc2" style="tab-stops:48.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619819"><span style="mso-no-proof:yes">5.1.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">      </span></span><span style="mso-no-proof:yes">Conclusiones</span><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619819 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">13<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800310039000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc2" style="tab-stops:48.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619820"><span style="mso-no-proof:yes">5.2.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">      </span></span><span style="mso-no-proof:yes">Líneas
de trabajo futuro</span><span style="color:windowtext;display:none;mso-hide:
screen;mso-no-proof:yes;text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619820 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">13<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800320030000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc1" style="tab-stops:right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619821"><span style="mso-no-proof:yes">Referencias bibliográficas</span><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted">. </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619821 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">14<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800320031000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="MsoToc1" style="tab-stops:60.0pt right dotted 453.05pt"><span lang="ES"><a href="#_Toc160619822"><span style="mso-no-proof:yes">Anexo A.</span><span style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast;
mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
color:windowtext;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes;text-decoration:none;text-underline:none'><span style="mso-tab-count:1">     </span></span><span style="mso-no-proof:yes">Código
fuente y datos analizados</span><span style="color:windowtext;display:none;
mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:none"><span style="mso-tab-count:1 dotted"> </span></span><!--[if supportFields]><span
style='color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none'><span style='mso-element:field-begin'></span>
PAGEREF _Toc160619822 \h <span style='mso-element:field-separator'></span></span><![endif]--><span style="color:windowtext;display:none;mso-hide:screen;mso-no-proof:yes;
text-decoration:none;text-underline:none">15<!--[if gte mso 9]><xml>
<w:data>08D0C9EA79F9BACE118C8200AA004BA90B02000000080000000E0000005F0054006F0063003100360030003600310039003800320032000000</w:data>
</xml><![endif]--></span><!--[if supportFields]><span style='color:windowtext;
display:none;mso-hide:screen;mso-no-proof:yes;text-decoration:none;text-underline:
none'><span style='mso-element:field-end'></span></span><![endif]--></a></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="Ttulondices"><!--[if supportFields]><span lang=ES style='font-size:
11.0pt;mso-bidi-font-size:12.0pt;line-height:150%;font-family:"Calibri",sans-serif;
mso-ascii-theme-font:minor-latin;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:
"Times New Roman";mso-fareast-language:ES'><span style='mso-element:field-end'></span></span><![endif]--><span lang="ES"><br clear="all" style="page-break-before:always"/>
Índice de figuras </span></p>
<p class="MsoTof" style="tab-stops:right dotted 453.05pt"><!--[if supportFields]><span
lang=ES style='mso-bidi-font-family:Arial;background:yellow;mso-highlight:yellow'><span
style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>TOC \f c \h \z \t &quot;Título
test
2025-12-16 00:00:58 +01:00
9;1;Figuras;1&quot; \c &quot;Figura&quot; <span style='mso-element:field-separator'></span></span><![endif]--><span lang="ES"></span><span lang="ES" style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
generation test
2025-12-15 23:54:41 +01:00
minor-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;
mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;mso-no-proof:yes'><o:p></o:p></span></p>
<p class="Ttulondices"><!--[if supportFields]><span lang=ES style='background:
yellow;mso-highlight:yellow'><span style='mso-element:field-end'></span></span><![endif]--><span lang="ES"><br clear="all" style="page-break-before:always"/>
Índice de tablas</span></p>
<p class="MsoTof" style="tab-stops:right dotted 453.05pt"><!--[if supportFields]><b
style='mso-bidi-font-weight:normal'><span lang=ES><span style='mso-element:
field-begin'></span><span style='mso-spacerun:yes'> </span>TOC \h \z \t
test
2025-12-16 00:00:58 +01:00
&quot;Tablas;1&quot; \c &quot;Tabla&quot; <span style='mso-element:field-separator'></span></span></b><![endif]--><span lang="ES"></span><span lang="ES" style='font-size:11.0pt;line-height:150%;mso-fareast-font-family:"Times New Roman";
generation test
2025-12-15 23:54:41 +01:00
mso-fareast-theme-font:minor-fareast;mso-bidi-font-family:"Times New Roman";
mso-bidi-theme-font:minor-bidi;mso-font-kerning:1.0pt;mso-ligatures:standardcontextual;
mso-no-proof:yes'><o:p></o:p></span></p>
<p class="Ttulondices"><!--[if supportFields]><b style='mso-bidi-font-weight:
normal'><span lang=ES style='mso-bidi-font-family:"Times New Roman"'><span
style='mso-element:field-end'></span></span></b><![endif]--><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:
"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
<p class="Ttulondices"><b style="mso-bidi-font-weight:normal"><span lang="ES" style='mso-bidi-font-family:"Times New Roman"'><o:p> </o:p></span></b></p>
</div>
<span lang="ES" style='font-size:18.0pt;line-height:150%;font-family:"Calibri Light",sans-serif;
mso-ascii-theme-font:major-latin;mso-fareast-font-family:"Times New Roman";
mso-hansi-theme-font:major-latin;mso-bidi-font-family:Arial;color:#0098CD;
mso-ansi-language:ES;mso-fareast-language:EN-US;mso-bidi-language:AR-SA'><br clear="all" style="page-break-before:always;mso-break-type:section-break"/>
</span>
<div class="WordSection2">
<h1 style="mso-list:l22 level1 lfo18"><a name="_Toc437515557"></a><a name="_Toc14106979"></a><a name="_Toc147741191"></a><a name="_Toc160619805"><span style="mso-bookmark:_Toc147741191"><span style="mso-bookmark:_Toc14106979"><span style="mso-bookmark:_Toc437515557"><?if !supportLists?><span lang="ES" style='mso-fareast-font-family:"Calibri Light";mso-fareast-theme-font:major-latin;
mso-bidi-font-family:"Calibri Light";mso-bidi-theme-font:major-latin'><span style="mso-list:Ignore">1.<span style='font:7.0pt "Times New Roman"'>  
</span></span></span><?endif?><span lang="ES" style="text-transform:none">Introducción</span></span></span></span></a><span style="mso-bookmark:_Toc14106979"><span style="mso-bookmark:_Toc437515557"><span lang="ES" style="text-transform:none"> </span></span></span></h1><p class="MsoNormal"><span lang="ES">Este capítulo presenta la motivación del trabajo, identificando el problema a resolver y justificando su relevancia. Se plantea la pregunta de investigación central y se describe la estructura del documento.</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">1.1. Motivación</span></h2>
<p class="MsoNormal"><span lang="ES">El Reconocimiento Óptico de Caracteres (OCR) es una tecnología fundamental en la era de la digitalización documental. Su capacidad para convertir imágenes de texto en datos editables y procesables ha transformado sectores como la administración pública, el ámbito legal, la banca y la educación. Sin embargo, a pesar de los avances significativos impulsados por el aprendizaje profundo, la implementación práctica de sistemas OCR de alta precisión sigue presentando desafíos considerables.</span></p>
<p class="MsoNormal"><span lang="ES">El procesamiento de documentos en español presenta particularidades que complican el reconocimiento automático de texto. Los caracteres especiales (ñ, acentos), las variaciones tipográficas en documentos académicos y administrativos, y la presencia de elementos gráficos como tablas, encabezados y marcas de agua generan errores que pueden propagarse en aplicaciones downstream como la extracción de entidades nombradas o el análisis semántico.</span></p>
<p class="MsoNormal"><span lang="ES">Los modelos OCR basados en redes neuronales profundas, como los empleados en PaddleOCR, EasyOCR o DocTR, ofrecen un rendimiento impresionante en benchmarks estándar. No obstante, su adaptación a dominios específicos típicamente requiere fine-tuning con datos etiquetados del dominio objetivo y recursos computacionales significativos (GPUs de alta capacidad). Esta barrera técnica y económica excluye a muchos investigadores y organizaciones de beneficiarse plenamente de estas tecnologías.</span></p>
<p class="MsoNormal"><span lang="ES">La presente investigación surge de una necesidad práctica: optimizar un sistema OCR para documentos académicos en español sin disponer de recursos GPU para realizar fine-tuning. Esta restricción, lejos de ser una limitación excepcional, representa la realidad de muchos entornos académicos y empresariales donde el acceso a infraestructura de cómputo avanzada es limitado.</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">1.2. Planteamiento del trabajo</span></h2>
<p class="MsoNormal"><span lang="ES">El problema central que aborda este trabajo puede formularse de la siguiente manera:</span></p>
<p class="MsoNormal" style="margin-left:2cm;margin-right:1cm"><i><span lang="ES">¿Es posible mejorar significativamente el rendimiento de modelos OCR preentrenados para documentos en español mediante la optimización sistemática de hiperparámetros, sin requerir fine-tuning ni recursos GPU?</span></i></p>
<p class="MsoNormal"><span lang="ES">Este planteamiento se descompone en las siguientes cuestiones específicas:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Selección de modelo base</b>: ¿Cuál de las soluciones OCR de código abierto disponibles (EasyOCR, PaddleOCR, DocTR) ofrece el mejor rendimiento base para documentos en español?</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Impacto de hiperparámetros</b>: ¿Qué hiperparámetros del pipeline OCR tienen mayor influencia en las métricas de error (CER, WER)?</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Optimización automatizada</b>: ¿Puede un proceso de búsqueda automatizada de hiperparámetros (mediante Ray Tune/Optuna) encontrar configuraciones que superen significativamente los valores por defecto?</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Viabilidad práctica</b>: ¿Son los tiempos de inferencia y los recursos requeridos compatibles con un despliegue en entornos con recursos limitados?</span></p>
<p class="MsoNormal"><span lang="ES">La relevancia de este problema radica en su aplicabilidad inmediata. Una metodología reproducible para optimizar OCR sin fine-tuning beneficiaría a:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Investigadores que procesan grandes volúmenes de documentos académicos</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Instituciones educativas que digitalizan archivos históricos</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Pequeñas y medianas empresas que automatizan flujos documentales</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Desarrolladores que integran OCR en aplicaciones con restricciones de recursos</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">1.3. Estructura del trabajo</span></h2>
<p class="MsoNormal"><span lang="ES">El presente documento se organiza en los siguientes capítulos:</span></p>
<p class="MsoNormal"><span lang="ES"><b>Capítulo 2 - Contexto y Estado del Arte</b>: Se presenta una revisión de las tecnologías OCR basadas en aprendizaje profundo, incluyendo las arquitecturas de detección y reconocimiento de texto, así como los trabajos previos en optimización de estos sistemas.</span></p>
<p class="MsoNormal"><span lang="ES"><b>Capítulo 3 - Objetivos y Metodología</b>: Se definen los objetivos SMART del trabajo y se describe la metodología experimental seguida, incluyendo la preparación del dataset, las métricas de evaluación y el proceso de optimización con Ray Tune.</span></p>
<p class="MsoNormal"><span lang="ES"><b>Capítulo 4 - Desarrollo Específico de la Contribución</b>: Este capítulo presenta el desarrollo completo del estudio comparativo y la optimización de hiperparámetros de sistemas OCR, estructurado en tres secciones: (4.1) planteamiento de la comparativa con la evaluación de EasyOCR, PaddleOCR y DocTR; (4.2) desarrollo de la comparativa con la optimización de hiperparámetros mediante Ray Tune; y (4.3) discusión y análisis de resultados.</span></p>
<p class="MsoNormal"><span lang="ES"><b>Capítulo 5 - Conclusiones y Trabajo Futuro</b>: Se resumen las contribuciones del trabajo, se discute el grado de cumplimiento de los objetivos y se proponen líneas de trabajo futuro.</span></p>
<p class="MsoNormal"><span lang="ES"><b>Anexos</b>: Se incluye el enlace al repositorio de código fuente y datos, así como tablas completas de resultados experimentales.</span></p><h1 style="mso-list:l22 level1 lfo18"><span style="mso-bookmark:_Toc14106979"><span style="mso-bookmark:_Toc437515557"><a name="_Toc147741195"></a><a name="_Toc160619809"><span style="mso-bookmark:_Toc147741195"><?if !supportLists?><span lang="ES" style='mso-fareast-font-family:"Calibri Light";mso-fareast-theme-font:
major-latin;mso-bidi-font-family:"Calibri Light";mso-bidi-theme-font:major-latin;
text-transform:none'><span style="mso-list:Ignore">2.<span style='font:7.0pt "Times New Roman"'>  
</span></span></span><?endif?><span lang="ES" style="text-transform:none">Contexto
y estado del arte</span></span></a></span></span><span style="mso-bookmark:
_Toc14106979"><span style="mso-bookmark:_Toc437515557"><span lang="ES" style="text-transform:none"><o:p></o:p></span></span></span></h1><p class="MsoNormal"><span lang="ES">Este capítulo presenta el marco teórico y tecnológico en el que se desarrolla el presente trabajo. Se revisan los fundamentos del Reconocimiento Óptico de Caracteres (OCR), la evolución de las técnicas basadas en aprendizaje profundo, las principales soluciones de código abierto disponibles y los trabajos previos relacionados con la optimización de sistemas OCR.</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">2.1. Contexto del problema</span></h2>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">Definición y Evolución Histórica del OCR</span></h3>
<p class="MsoNormal"><span lang="ES">El Reconocimiento Óptico de Caracteres (OCR) es el proceso de conversión de imágenes de texto manuscrito, mecanografiado o impreso en texto codificado digitalmente. La tecnología OCR ha evolucionado significativamente desde sus orígenes en la década de 1950:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Primera generación (1950-1970)</b>: Sistemas basados en plantillas que requerían fuentes específicas.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Segunda generación (1970-1990)</b>: Introducción de técnicas de extracción de características y clasificadores estadísticos.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Tercera generación (1990-2010)</b>: Modelos basados en Redes Neuronales Artificiales y Modelos Ocultos de Markov (HMM).</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Cuarta generación (2010-presente)</b>: Arquitecturas de aprendizaje profundo que dominan el estado del arte.</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">Pipeline Moderno de OCR</span></h3>
<p class="MsoNormal"><span lang="ES">Los sistemas OCR modernos siguen típicamente un pipeline de dos etapas:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES"><b>Figura 1.</b> <i>Diagrama 1</i></span></p>
<p class="MsoNormal" style="text-align:center"><span lang="ES"><img alt="Diagrama 1" src="figures/figura_1.png" width="500"/></span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Detección de texto (Text Detection)</b>: Localización de regiones que contienen texto en la imagen. Las arquitecturas más utilizadas incluyen:</span></p>
<p class="MsoNormal"><span lang="ES"> - EAST (Efficient and Accurate Scene Text Detector) - CRAFT (Character Region Awareness for Text Detection) - DB (Differentiable Binarization)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Reconocimiento de texto (Text Recognition)</b>: Transcripción del contenido textual de las regiones detectadas. Las arquitecturas predominantes son:</span></p>
<p class="MsoNormal"><span lang="ES"> - CRNN (Convolutional Recurrent Neural Network) con CTC loss - Arquitecturas encoder-decoder con atención - Transformers (ViTSTR, TrOCR)</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">Métricas de Evaluación</span></h3>
<p class="MsoNormal"><span lang="ES">Las métricas estándar para evaluar sistemas OCR son:</span></p>
<p class="MsoNormal"><span lang="ES"><b>Character Error Rate (CER)</b>: Se calcula como CER = (S + D + I) / N, donde S = sustituciones, D = eliminaciones, I = inserciones, N = caracteres de referencia.</span></p>
<p class="MsoNormal"><span lang="ES"><b>Word Error Rate (WER)</b>: Se calcula de forma análoga pero a nivel de palabras en lugar de caracteres.</span></p>
<p class="MsoNormal"><span lang="ES">Un CER del 1% significa que 1 de cada 100 caracteres es erróneo. Para aplicaciones críticas como extracción de datos financieros o médicos, se requieren CER inferiores al 1%.</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">Particularidades del OCR para el Idioma Español</span></h3>
<p class="MsoNormal"><span lang="ES">El español presenta características específicas que impactan el OCR:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Caracteres especiales</b>: ñ, á, é, í, ó, ú, ü, ¿, ¡</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Diacríticos</b>: Los acentos pueden confundirse con ruido o artefactos</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Longitud de palabras</b>: Palabras generalmente más largas que en inglés</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Puntuación</b>: Signos de interrogación y exclamación invertidos</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">2.2. Estado del arte</span></h2>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">Soluciones OCR de Código Abierto</span></h3>
<h4><span lang="ES">EasyOCR</span></h4>
<p class="MsoNormal"><span lang="ES">EasyOCR es una biblioteca de OCR desarrollada por Jaided AI (2020) que soporta más de 80 idiomas. Sus características principales incluyen:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Arquitectura</b>: Detector CRAFT + Reconocedor CRNN/Transformer</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Fortalezas</b>: Facilidad de uso, soporte multilingüe amplio, bajo consumo de memoria</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Limitaciones</b>: Menor precisión en documentos complejos, opciones de configuración limitadas</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Caso de uso ideal</b>: Prototipado rápido y aplicaciones con restricciones de memoria</span></p>
<h4><span lang="ES">PaddleOCR</span></h4>
<p class="MsoNormal"><span lang="ES">PaddleOCR es el sistema OCR desarrollado por Baidu como parte del ecosistema PaddlePaddle (2024). La versión PP-OCRv5, utilizada en este trabajo, representa el estado del arte en OCR industrial:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Arquitectura</b>:</span></p>
<p class="MsoNormal"><span lang="ES"> - Detector: DB (Differentiable Binarization) con backbone ResNet (Liao et al., 2020) - Reconocedor: SVTR (Scene-Text Visual Transformer Recognition) - Clasificador de orientación opcional</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Hiperparámetros configurables</b>:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 1.</b> <i>Hiperparámetros configurables de PaddleOCR.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Parámetro</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Descripción</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Valor por defecto</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Umbral de detección de píxeles</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">0.3</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_box_thresh</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Umbral de caja de detección</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">0.6</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_unclip_ratio</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Coeficiente de expansión</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">1.5</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_rec_score_thresh</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Umbral de confianza de reconocimiento</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">0.5</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_textline_orientation</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Clasificación de orientación</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">False</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_orientation_classify</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Clasificación de orientación de documento</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">False</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_unwarping</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Corrección de deformación<
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Fortalezas</b>: Alta precisión, pipeline altamente configurable, modelos específicos para servidor</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Limitaciones</b>: Mayor complejidad de configuración, dependencia del framework PaddlePaddle</span></p>
<h4><span lang="ES">DocTR</span></h4>
<p class="MsoNormal"><span lang="ES">DocTR (Document Text Recognition) es una biblioteca desarrollada por Mindee (2021) orientada a la investigación:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Arquitectura</b>:</span></p>
<p class="MsoNormal"><span lang="ES"> - Detectores: DB, LinkNet - Reconocedores: CRNN, SAR, ViTSTR</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Fortalezas</b>: API limpia, orientación académica, salida estructurada de alto nivel</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Limitaciones</b>: Menor rendimiento en español comparado con PaddleOCR</span></p>
<h4><span lang="ES">Comparativa de Arquitecturas</span></h4>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 2.</b> <i>Comparativa de soluciones OCR de código abierto.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Modelo</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Tipo</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Componentes</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Fortalezas Clave</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>EasyOCR</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">End-to-end (det + rec)</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">CRAFT + CRNN/Transformer</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Ligero, fácil de usar, multilingüe</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>PaddleOCR</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">End-to-end (det + rec + cls)</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">DB + SVTR/CRNN</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Soporte multilingüe robusto, configurable</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>DocTR</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">End-to-end (det + rec)</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">DB/LinkNet + CRNN/SAR/ViTSTR</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Orientado a investigación, API limpia</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">Optimización de Hiperparámetros</span></h3>
<h4><span lang="ES">Fundamentos</span></h4>
<p class="MsoNormal"><span lang="ES">La optimización de hiperparámetros (HPO) busca encontrar la configuración de parámetros que maximiza (o minimiza) una métrica objetivo (Feurer &amp; Hutter, 2019). A diferencia de los parámetros del modelo (pesos), los hiperparámetros no se aprenden durante el entrenamiento.</span></p>
<p class="MsoNormal"><span lang="ES">Los métodos de HPO incluyen:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Grid Search</b>: Búsqueda exhaustiva en una rejilla predefinida</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Random Search</b>: Muestreo aleatorio del espacio de búsqueda (Bergstra &amp; Bengio, 2012)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Bayesian Optimization</b>: Modelado probabilístico de la función objetivo (Bergstra et al., 2011)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Algoritmos evolutivos</b>: Optimización inspirada en evolución biológica</span></p>
<h4><span lang="ES">Ray Tune y Optuna</span></h4>
<p class="MsoNormal"><span lang="ES"><b>Ray Tune</b> es un framework de optimización de hiperparámetros escalable (Liaw et al., 2018) que permite:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Ejecución paralela de experimentos</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Early stopping de configuraciones poco prometedoras</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Integración con múltiples algoritmos de búsqueda</span></p>
<p class="MsoNormal"><span lang="ES"><b>Optuna</b> es una biblioteca de optimización bayesiana (Akiba et al., 2019) que implementa:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Tree-structured Parzen Estimator (TPE)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Pruning de trials no prometedores</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Visualización de resultados</span></p>
<p class="MsoNormal"><span lang="ES">La combinación Ray Tune + Optuna permite búsquedas eficientes en espacios de alta dimensionalidad.</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES"><b>Figura 2.</b> <i>Diagrama 2</i></span></p>
<p class="MsoNormal" style="text-align:center"><span lang="ES"><img alt="Diagrama 2" src="figures/figura_2.png" width="500"/></span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">HPO en Sistemas OCR</span></h4>
<p class="MsoNormal"><span lang="ES">La aplicación de HPO a sistemas OCR ha sido explorada principalmente en el contexto de:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Preprocesamiento de imagen</b>: Optimización de parámetros de binarización, filtrado y escalado (Liang et al., 2005)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Arquitecturas de detección</b>: Ajuste de umbrales de confianza y NMS (Non-Maximum Suppression)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Post-procesamiento</b>: Optimización de corrección ortográfica y modelos de lenguaje</span></p>
<p class="MsoNormal"><span lang="ES">Sin embargo, existe un vacío en la literatura respecto a la optimización sistemática de los hiperparámetros de inferencia en pipelines OCR modernos como PaddleOCR, especialmente para idiomas diferentes del inglés y chino.</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">Datasets y Benchmarks para Español</span></h3>
<p class="MsoNormal"><span lang="ES">Los principales recursos para evaluación de OCR en español incluyen:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>FUNSD-ES</b>: Versión en español del dataset de formularios</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>MLT (ICDAR)</b>: Multi-Language Text dataset con muestras en español</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Documentos académicos</b>: Utilizados en este trabajo (instrucciones TFE de UNIR)</span></p>
<p class="MsoNormal"><span lang="ES">Los trabajos previos en OCR para español se han centrado principalmente en:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span>Digitalización de archivos históricos (manuscritos coloniales)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">2.<span style="font-size:7pt">   </span>Procesamiento de documentos de identidad</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">3.<span style="font-size:7pt">   </span>Reconocimiento de texto en escenas naturales</span></p>
<p class="MsoNormal"><span lang="ES">La optimización de hiperparámetros para documentos académicos en español representa una contribución original de este trabajo.</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">2.3. Conclusiones</span></h2>
<p class="MsoNormal"><span lang="ES">Este capítulo ha presentado:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span>Los fundamentos del OCR moderno y su pipeline de detección-reconocimiento</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">2.<span style="font-size:7pt">   </span>Las tres principales soluciones de código abierto: EasyOCR, PaddleOCR y DocTR</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">3.<span style="font-size:7pt">   </span>Los métodos de optimización de hiperparámetros, con énfasis en Ray Tune y Optuna</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">4.<span style="font-size:7pt">   </span>Las particularidades del OCR para el idioma español</span></p>
<p class="MsoNormal"><span lang="ES">El estado del arte revela que, si bien existen soluciones OCR de alta calidad, su optimización para dominios específicos mediante ajuste de hiperparámetros (sin fine-tuning) ha recibido poca atención. Este trabajo contribuye a llenar ese vacío proponiendo una metodología reproducible para la optimización de PaddleOCR en documentos académicos en español.</span></p><h1 style="mso-list:l22 level1 lfo18"><span style="mso-bookmark:_Toc14106979"><span style="mso-bookmark:_Toc437515557"><a name="_Toc147741196"></a><a name="_Toc160619813"><span style="mso-bookmark:_Toc147741196"><?if !supportLists?><span lang="ES" style='mso-fareast-font-family:"Calibri Light";mso-fareast-theme-font:
major-latin;mso-bidi-font-family:"Calibri Light";mso-bidi-theme-font:major-latin;
text-transform:none'><span style="mso-list:Ignore">3.<span style='font:7.0pt "Times New Roman"'>  
</span></span></span><?endif?><span lang="ES" style="text-transform:none">Objetivos
concretos y metodología de trabajo</span></span></a></span></span><span style="mso-bookmark:_Toc14106979"><span style="mso-bookmark:_Toc437515557"><span lang="ES" style="text-transform:none"><o:p></o:p></span></span></span></h1><p class="MsoNormal"><span lang="ES">Este capítulo establece los objetivos del trabajo siguiendo la metodología SMART (Doran, 1981) y describe la metodología experimental empleada para alcanzarlos. Se define un objetivo general y cinco objetivos específicos, todos ellos medibles y verificables.</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">3.1. Objetivo general</span></h2>
<p class="MsoNormal" style="margin-left:2cm;margin-right:1cm"><i><span lang="ES"><b>Optimizar el rendimiento de PaddleOCR para documentos académicos en español mediante ajuste de hiperparámetros, alcanzando un CER inferior al 2% sin requerir fine-tuning del modelo ni recursos GPU dedicados.</b></span></i></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">Justificación SMART del Objetivo General</span></h3>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 3.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Criterio</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Cumplimiento</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>Específico (S)</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Se define claramente qué se quiere lograr: optimizar PaddleOCR mediante ajuste de hiperparámetros para documentos en español</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>Medible (M)</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Se establece una métrica cuantificable: CER &lt; 2%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>Alcanzable (A)</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Es viable dado que: (1) PaddleOCR permite configuración de hiperparámetros, (2) Ray Tune posibilita búsqueda automatizada, (3) No se requiere GPU</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>Relevante (R)</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">El impacto es demostrable: mejora la extracción de texto en documentos académicos sin costes adicionales de infraestructura</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>Temporal (T)</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">El plazo es un cuatrimestre, correspondiente al TFM</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">3.2. Objetivos específicos</span></h2>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">OE1: Comparar soluciones OCR de código abierto</span></h3>
<p class="MsoNormal" style="margin-left:2cm;margin-right:1cm"><i><span lang="ES"><b>Evaluar el rendimiento base de EasyOCR, PaddleOCR y DocTR en documentos académicos en español, utilizando CER y WER como métricas, para seleccionar el modelo más prometedor.</b></span></i></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">OE2: Preparar un dataset de evaluación</span></h3>
<p class="MsoNormal" style="margin-left:2cm;margin-right:1cm"><i><span lang="ES"><b>Construir un dataset estructurado de imágenes de documentos académicos en español con su texto de referencia (ground truth) extraído del PDF original.</b></span></i></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">OE3: Identificar hiperparámetros críticos</span></h3>
<p class="MsoNormal" style="margin-left:2cm;margin-right:1cm"><i><span lang="ES"><b>Analizar la correlación entre los hiperparámetros de PaddleOCR y las métricas de error para identificar los parámetros con mayor impacto en el rendimiento.</b></span></i></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">OE4: Optimizar hiperparámetros con Ray Tune</span></h3>
<p class="MsoNormal" style="margin-left:2cm;margin-right:1cm"><i><span lang="ES"><b>Ejecutar una búsqueda automatizada de hiperparámetros utilizando Ray Tune con Optuna, evaluando al menos 50 configuraciones diferentes.</b></span></i></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">OE5: Validar la configuración optimizada</span></h3>
<p class="MsoNormal" style="margin-left:2cm;margin-right:1cm"><i><span lang="ES"><b>Comparar el rendimiento de la configuración baseline versus la configuración optimizada sobre el dataset completo, documentando la mejora obtenida.</b></span></i></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">3.3. Metodología del trabajo</span></h2>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">3.3.1. Visión General</span></h3>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES"><b>Figura 3.</b> <i>Diagrama 3</i></span></p>
<p class="MsoNormal" style="text-align:center"><span lang="ES"><img alt="Diagrama 3" src="figures/figura_3.png" width="500"/></span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">3.3.2. Fase 1: Preparación del Dataset</span></h3>
<h4><span lang="ES">Fuente de Datos</span></h4>
<p class="MsoNormal"><span lang="ES">Se utilizaron documentos PDF académicos de UNIR (Universidad Internacional de La Rioja), específicamente las instrucciones para la elaboración del TFE del Máster en Inteligencia Artificial.</span></p>
<h4><span lang="ES">Proceso de Conversión</span></h4>
<p class="MsoNormal"><span lang="ES">El script <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">prepare_dataset.ipynb</span> implementa:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Conversión PDF a imágenes</b>:</span></p>
<p class="MsoNormal"><span lang="ES"> - Biblioteca: PyMuPDF (fitz) - Resolución: 300 DPI - Formato de salida: PNG</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Extracción de texto de referencia</b>:</span></p>
<p class="MsoNormal"><span lang="ES"> - Método: <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">page.get_text("dict")</span> de PyMuPDF - Preservación de estructura de líneas - Tratamiento de texto vertical/marginal - Normalización de espacios y saltos de línea</span></p>
<h4><span lang="ES">Estructura del Dataset</span></h4>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES"><b>Figura 4.</b> <i>Diagrama 4</i></span></p>
<p class="MsoNormal" style="text-align:center"><span lang="ES"><img alt="Diagrama 4" src="figures/figura_4.png" width="500"/></span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Clase ImageTextDataset</span></h4>
<p class="MsoNormal"><span lang="ES">Se implementó una clase Python para cargar pares imagen-texto:</span></p>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>class ImageTextDataset:
def __init__(self, root):
# Carga pares (imagen, texto) de carpetas pareadas
def __getitem__(self, idx):
# Retorna (PIL.Image, str)</pre></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">3.3.3. Fase 2: Benchmark Comparativo</span></h3>
<h4><span lang="ES">Modelos Evaluados</span></h4>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 4.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Modelo</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Versión</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Configuración</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">EasyOCR</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">-</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Idiomas: ['es', 'en']</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PaddleOCR</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PP-OCRv5</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Modelos server_det + server_rec</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">DocTR</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">-</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">db_resnet50 + sar_resnet31</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Métricas de Evaluación</span></h4>
<p class="MsoNormal"><span lang="ES">Se utilizó la biblioteca <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">jiwer</span> para calcular:</span></p>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>from jiwer import wer, cer
def evaluate_text(reference, prediction):
return {
'WER': wer(reference, prediction),
'CER': cer(reference, prediction)
}</pre></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">3.3.4. Fase 3: Espacio de Búsqueda</span></h3>
<h4><span lang="ES">Hiperparámetros Seleccionados</span></h4>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 5.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Parámetro</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Tipo</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Rango/Valores</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Descripción</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_orientation_classify</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Booleano</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">[True, False]</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Clasificación de orientación del documento</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_unwarping</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Booleano</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">[True, False]</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Corrección de deformación del documento</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">textline_orientation</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Booleano</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">[True, False]</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Clasificación de orientación de línea de texto</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Continuo</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">[0.0, 0.7]</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Umbral de detección de píxeles de texto</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_box_thresh</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Continuo</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">[0.0, 0.7]</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Umbral de caja de detección</span></
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Configuración de Ray Tune</span></h4>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>from ray import tune
from ray.tune.search.optuna import OptunaSearch
search_space = {
"use_doc_orientation_classify": tune.choice([True, False]),
"use_doc_unwarping": tune.choice([True, False]),
"textline_orientation": tune.choice([True, False]),
"text_det_thresh": tune.uniform(0.0, 0.7),
"text_det_box_thresh": tune.uniform(0.0, 0.7),
"text_det_unclip_ratio": tune.choice([0.0]),
"text_rec_score_thresh": tune.uniform(0.0, 0.7),
}
tuner = tune.Tuner(
trainable_paddle_ocr,
tune_config=tune.TuneConfig(
metric="CER",
mode="min",
search_alg=OptunaSearch(),
num_samples=64,
max_concurrent_trials=2
)
)</pre></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">3.3.5. Fase 4: Ejecución de Optimización</span></h3>
<h4><span lang="ES">Arquitectura de Ejecución</span></h4>
<p class="MsoNormal"><span lang="ES">Debido a incompatibilidades entre Ray y PaddleOCR en el mismo proceso, se implementó una arquitectura basada en subprocesos:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES"><b>Figura 5.</b> <i>Diagrama 5</i></span></p>
<p class="MsoNormal" style="text-align:center"><span lang="ES"><img alt="Diagrama 5" src="figures/figura_5.png" width="500"/></span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Script de Evaluación (paddle_ocr_tuning.py)</span></h4>
<p class="MsoNormal"><span lang="ES">El script recibe hiperparámetros por línea de comandos:</span></p>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>python paddle_ocr_tuning.py \
--pdf-folder ./dataset \
--textline-orientation True \
--text-det-box-thresh 0.5 \
--text-det-thresh 0.4 \
--text-rec-score-thresh 0.6</pre></span></p>
<p class="MsoNormal"><span lang="ES">Y retorna métricas en formato JSON:</span></p>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>{
"CER": 0.0125,
"WER": 0.1040,
"TIME": 331.09,
"PAGES": 5,
"TIME_PER_PAGE": 66.12
}</pre></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">3.3.6. Fase 5: Validación</span></h3>
<h4><span lang="ES">Protocolo de Validación</span></h4>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Baseline</b>: Ejecución con configuración por defecto de PaddleOCR</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">2.<span style="font-size:7pt">   </span><b>Optimizado</b>: Ejecución con mejor configuración encontrada</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">3.<span style="font-size:7pt">   </span><b>Comparación</b>: Evaluación sobre las 24 páginas del dataset completo</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">4.<span style="font-size:7pt">   </span><b>Métricas reportadas</b>: CER, WER, tiempo de procesamiento</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">3.3.7. Entorno de Ejecución</span></h3>
<h4><span lang="ES">Hardware</span></h4>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 6.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Componente</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Especificación</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">CPU</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Intel Core (especificar modelo)</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">RAM</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">16 GB</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">GPU</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">No disponible (ejecución en CPU)</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Almacenamiento</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">SSD</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Software</span></h4>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 7.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Componente</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Versión</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Sistema Operativo</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Windows 10/11</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Python</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">3.11.9</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PaddleOCR</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">3.3.2</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PaddlePaddle</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">3.2.2</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Ray</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">2.52.1</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Optuna</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">4.6.0</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">3.3.8. Limitaciones Metodológicas</span></h3>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Tamaño del dataset</b>: El dataset contiene 24 páginas de un único tipo de documento. Resultados pueden no generalizar a otros formatos.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Ejecución en CPU</b>: Los tiempos de procesamiento (~70s/página) serían significativamente menores con GPU.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Ground truth imperfecto</b>: El texto de referencia extraído de PDF puede contener errores en documentos con layouts complejos.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Parámetro fijo</b>: <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_unclip_ratio</span> quedó fijado en 0.0 durante todo el experimento por decisión de diseño inicial.</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">3.4. Resumen del capítulo</span></h2>
<p class="MsoNormal"><span lang="ES">Este capítulo ha establecido:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span>Un objetivo general SMART: alcanzar CER &lt; 2% mediante optimización de hiperparámetros</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">2.<span style="font-size:7pt">   </span>Cinco objetivos específicos medibles y alcanzables</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">3.<span style="font-size:7pt">   </span>Una metodología experimental en cinco fases claramente definidas</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">4.<span style="font-size:7pt">   </span>El espacio de búsqueda de hiperparámetros y la configuración de Ray Tune</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">5.<span style="font-size:7pt">   </span>Las limitaciones reconocidas del enfoque</span></p>
<p class="MsoNormal"><span lang="ES">El siguiente capítulo presenta el desarrollo específico de la contribución, incluyendo el benchmark comparativo de soluciones OCR, la optimización de hiperparámetros y el análisis de resultados.</span></p><p class="MsoNormal" style="margin-left:18.0pt;text-indent:-18.0pt;page-break-after:
avoid;mso-outline-level:1;mso-list:l22 level1 lfo18"><span style="mso-bookmark:
_Toc14106979"><span style="mso-bookmark:_Toc437515557"><a name="_Toc160619817"></a><a name="_Toc100036217"><span style="mso-bookmark:_Toc160619817"><?if !supportLists?><span lang="ES" style='font-size:18.0pt;mso-bidi-font-size:16.0pt;line-height:150%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-fareast-font-family:"Calibri Light";mso-fareast-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-theme-font:major-latin;color:#0098CD;
mso-font-kerning:16.0pt;mso-bidi-font-weight:bold'><span style="mso-list:Ignore">4.<span style='font:7.0pt "Times New Roman"'>   </span></span></span><?endif?><span lang="ES" style='font-size:18.0pt;mso-bidi-font-size:16.0pt;line-height:150%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:"Times New Roman";
color:#0098CD;text-transform:uppercase;mso-font-kerning:16.0pt;mso-bidi-font-weight:
bold'>D</span></span></a></span></span><span style="mso-bookmark:_Toc14106979"><span style="mso-bookmark:_Toc437515557"><span style="mso-bookmark:_Toc100036217"><span style="mso-bookmark:_Toc160619817"><span lang="ES" style='font-size:18.0pt;
mso-bidi-font-size:16.0pt;line-height:150%;font-family:"Calibri Light",sans-serif;
mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin;mso-bidi-font-family:
"Times New Roman";color:#0098CD;mso-font-kerning:16.0pt;mso-bidi-font-weight:
bold'>es</span></span></span></span></span><span style="mso-bookmark:_Toc14106979"><span style="mso-bookmark:_Toc437515557"><span style="mso-bookmark:_Toc160619817"><span lang="ES" style='font-size:18.0pt;mso-bidi-font-size:16.0pt;line-height:150%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:"Times New Roman";
color:#0098CD;mso-font-kerning:16.0pt;mso-bidi-font-weight:bold'>arrollo
específico de la contribución</span></span></span></span><span style="mso-bookmark:_Toc14106979"><span style="mso-bookmark:_Toc437515557"><span lang="ES" style='font-size:18.0pt;mso-bidi-font-size:16.0pt;line-height:150%;
font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:major-latin;
mso-hansi-theme-font:major-latin;mso-bidi-font-family:"Times New Roman";
color:#0098CD;mso-font-kerning:16.0pt;mso-bidi-font-weight:bold'><o:p></o:p></span></span></span></p><p class="MsoNormal"><span lang="ES">Este capítulo presenta el desarrollo completo del estudio comparativo y la optimización de hiperparámetros de sistemas OCR. Se estructura según el tipo de trabajo "Comparativa de soluciones" establecido por las instrucciones de UNIR: planteamiento de la comparativa, desarrollo de la comparativa, y discusión y análisis de resultados.</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">4.1. Planteamiento de la comparativa</span></h2>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.1.1. Introducción</span></h3>
<p class="MsoNormal"><span lang="ES">Esta sección presenta los resultados del estudio comparativo realizado entre tres soluciones OCR de código abierto: EasyOCR, PaddleOCR y DocTR. Los experimentos fueron documentados en el notebook <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">ocr_benchmark_notebook.ipynb</span> del repositorio. El objetivo es identificar el modelo base más prometedor para la posterior fase de optimización de hiperparámetros.</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.1.2. Configuración del Experimento</span></h3>
<h4><span lang="ES">Dataset de Evaluación</span></h4>
<p class="MsoNormal"><span lang="ES">Se utilizó el documento "Instrucciones para la redacción y elaboración del TFE" del Máster Universitario en Inteligencia Artificial de UNIR, ubicado en la carpeta <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">instructions/</span>.</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 8.</b> <i>Tabla 3. Características del dataset de evaluación.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Característica</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Valor</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Número de páginas evaluadas</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">5 (páginas 1-5 en benchmark inicial)</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Formato</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PDF digital (no escaneado)</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Idioma</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Español</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Resolución de conversión</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">300 DPI</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Configuración de los Modelos</span></h4>
<p class="MsoNormal"><span lang="ES">Según el código en <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">ocr_benchmark_notebook.ipynb</span>:</span></p>
<p class="MsoNormal"><span lang="ES"><b>EasyOCR</b>:</span></p>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>easyocr_reader = easyocr.Reader(['es', 'en']) # Spanish and English</pre></span></p>
<p class="MsoNormal"><span lang="ES"><b>PaddleOCR (PP-OCRv5)</b>:</span></p>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>paddleocr_model = PaddleOCR(
text_detection_model_name="PP-OCRv5_server_det",
text_recognition_model_name="PP-OCRv5_server_rec",
use_doc_orientation_classify=False,
use_doc_unwarping=False,
use_textline_orientation=True,
)</pre></span></p>
<p class="MsoNormal"><span lang="ES">Versión utilizada: PaddleOCR 3.2.0 (según output del notebook)</span></p>
<p class="MsoNormal"><span lang="ES"><b>DocTR</b>:</span></p>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>doctr_model = ocr_predictor(det_arch="db_resnet50", reco_arch="sar_resnet31", pretrained=True)</pre></span></p>
<h4><span lang="ES">Métricas de Evaluación</span></h4>
<p class="MsoNormal"><span lang="ES">Se utilizó la biblioteca <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">jiwer</span> para calcular CER y WER:</span></p>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>from jiwer import wer, cer
def evaluate_text(reference, prediction):
return {'WER': wer(reference, prediction), 'CER': cer(reference, prediction)}</pre></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.1.3. Resultados del Benchmark</span></h3>
<h4><span lang="ES">Resultados de PaddleOCR (Datos del CSV)</span></h4>
<p class="MsoNormal"><span lang="ES">Del archivo <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">results/ai_ocr_benchmark_finetune_results_20251206_113206.csv</span>, se obtienen los siguientes resultados de PaddleOCR para las páginas 5-9 del documento:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 9.</b> <i>Tabla 4. Resultados de PaddleOCR por página (benchmark inicial).</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Página</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">WER</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">CER</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">5</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">12.16%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">6.33%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">6</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">12.81%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">6.40%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">7</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">11.06%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">6.24%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">8</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">8.13%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">1.54%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">9</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">10.61%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">5.58%</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="ES"><b>Promedio PaddleOCR (páginas 5-9):</b></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">CER medio: ~5.22%</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">WER medio: ~10.95%</span></p>
<h4><span lang="ES">Comparativa de Modelos</span></h4>
<p class="MsoNormal"><span lang="ES">Según la documentación del notebook <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">ocr_benchmark_notebook.ipynb</span>, los tres modelos evaluados representan diferentes paradigmas de OCR:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 10.</b> <i>Tabla 5. Comparativa de arquitecturas OCR evaluadas.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Modelo</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Tipo</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Componentes</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Fortalezas Clave</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>EasyOCR</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">End-to-end (det + rec)</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">DB + CRNN/Transformer</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Ligero, fácil de usar, multilingüe</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>PaddleOCR (PP-OCR)</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">End-to-end (det + rec + cls)</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">DB + SRN/CRNN</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Soporte multilingüe robusto, pipeline configurable</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>DocTR</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">End-to-end (det + rec)</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">DB/LinkNet + CRNN/SAR/VitSTR</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Orientado a investigación, API limpia</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Ejemplo de Salida OCR</span></h4>
<p class="MsoNormal"><span lang="ES">Del archivo CSV, un ejemplo de predicción de PaddleOCR para la página 8:</span></p>
<p class="MsoNormal" style="margin-left:2cm;margin-right:1cm"><i><span lang="ES">"Escribe siempre al menos un párrafo de introducción en cada capítulo o apartado, explicando de qué vas a tratar en esa sección. Evita que aparezcan dos encabezados de nivel consecutivos sin ningún texto entre medias. [...] En esta titulacióon se cita de acuerdo con la normativa Apa."</span></i></p>
<p class="MsoNormal"><span lang="ES"><b>Errores observados en este ejemplo:</b></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">titulacióon</span> en lugar de <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">titulación</span> (carácter duplicado)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">Apa</span> en lugar de <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">APA</span> (capitalización)</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.1.4. Justificación de la Selección de PaddleOCR</span></h3>
<h4><span lang="ES">Criterios de Selección</span></h4>
<p class="MsoNormal"><span lang="ES">Basándose en los resultados obtenidos y la documentación del benchmark:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Rendimiento</b>: PaddleOCR obtuvo CER entre 1.54% y 6.40% en las páginas evaluadas</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">2.<span style="font-size:7pt">   </span><b>Configurabilidad</b>: PaddleOCR ofrece múltiples hiperparámetros ajustables:</span></p>
<p class="MsoNormal"><span lang="ES"> - Umbrales de detección (<span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span>, <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_box_thresh</span>) - Umbral de reconocimiento (<span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_rec_score_thresh</span>) - Componentes opcionales (<span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_textline_orientation</span>, <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_orientation_classify</span>, <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_unwarping</span>)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Documentación oficial</b>: [PaddleOCR Documentation](https://www.paddleocr.ai/v3.0.0/en/version3.x/pipeline_usage/OCR.html)</span></p>
<h4><span lang="ES">Decisión</span></h4>
<p class="MsoNormal"><span lang="ES"><b>Se selecciona PaddleOCR (PP-OCRv5)</b> para la fase de optimización debido a:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Resultados iniciales prometedores (CER ~5%)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Alta configurabilidad de hiperparámetros de inferencia</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Pipeline modular que permite experimentación</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.1.5. Limitaciones del Benchmark</span></h3>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Tamaño reducido</b>: Solo 5 páginas evaluadas en el benchmark comparativo inicial</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">2.<span style="font-size:7pt">   </span><b>Único tipo de documento</b>: Documentos académicos de UNIR únicamente</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">3.<span style="font-size:7pt">   </span><b>Ground truth</b>: El texto de referencia se extrajo automáticamente del PDF, lo cual puede introducir errores en layouts complejos</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.1.6. Resumen de la Sección</span></h3>
<p class="MsoNormal"><span lang="ES">Esta sección ha presentado:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span>La configuración del benchmark según <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">ocr_benchmark_notebook.ipynb</span></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">2.<span style="font-size:7pt">   </span>Los resultados cuantitativos de PaddleOCR del archivo CSV de resultados</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">3.<span style="font-size:7pt">   </span>La justificación de la selección de PaddleOCR para optimización</span></p>
<p class="MsoNormal"><span lang="ES"><b>Fuentes de datos utilizadas:</b></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">ocr_benchmark_notebook.ipynb</span>: Código del benchmark</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">results/ai_ocr_benchmark_finetune_results_20251206_113206.csv</span>: Resultados numéricos</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Documentación oficial de PaddleOCR</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">4.2. Desarrollo de la comparativa: Optimización de hiperparámetros</span></h2>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.2.1. Introducción</span></h3>
<p class="MsoNormal"><span lang="ES">Esta sección describe el proceso de optimización de hiperparámetros de PaddleOCR utilizando Ray Tune con el algoritmo de búsqueda Optuna. Los experimentos fueron implementados en el notebook <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/paddle_ocr_fine_tune_unir_raytune.ipynb</span> y los resultados se almacenaron en <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/raytune_paddle_subproc_results_20251207_192320.csv</span>.</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.2.2. Configuración del Experimento</span></h3>
<h4><span lang="ES">Entorno de Ejecución</span></h4>
<p class="MsoNormal"><span lang="ES">Según los outputs del notebook:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 11.</b> <i>Tabla 6. Entorno de ejecución del experimento.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Componente</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Versión/Especificación</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Python</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">3.11.9</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PaddlePaddle</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">3.2.2</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PaddleOCR</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">3.3.2</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Ray</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">2.52.1</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">GPU</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">No disponible (CPU only)</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Dataset</span></h4>
<p class="MsoNormal"><span lang="ES">Se utilizó un dataset estructurado en <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/dataset/</span> creado mediante el notebook <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/prepare_dataset.ipynb</span>:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Estructura</b>: Carpetas con subcarpetas <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">img/</span> y <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">txt/</span> pareadas</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Páginas evaluadas por trial</b>: 5 (páginas 5-10 del documento)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Gestión de datos</b>: Clase <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">ImageTextDataset</span> en <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/dataset_manager.py</span></span></p>
<h4><span lang="ES">Espacio de Búsqueda</span></h4>
<p class="MsoNormal"><span lang="ES">Según el código del notebook, se definió el siguiente espacio de búsqueda:</span></p>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>search_space = {
"use_doc_orientation_classify": tune.choice([True, False]),
"use_doc_unwarping": tune.choice([True, False]),
"textline_orientation": tune.choice([True, False]),
"text_det_thresh": tune.uniform(0.0, 0.7),
"text_det_box_thresh": tune.uniform(0.0, 0.7),
"text_det_unclip_ratio": tune.choice([0.0]), # Fijado
"text_rec_score_thresh": tune.uniform(0.0, 0.7),
}</pre></span></p>
<p class="MsoNormal"><span lang="ES"><b>Descripción de parámetros</b> (según documentación de PaddleOCR):</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 12.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Parámetro</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Descripción</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_orientation_classify</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Clasificación de orientación del documento</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_unwarping</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Corrección de deformación del documento</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">textline_orientation</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Clasificación de orientación de línea de texto</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Umbral de detección de píxeles de texto</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_box_thresh</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Umbral de caja de detección</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_unclip_ratio</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Coeficiente de expansión (fijado en 0.0)</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_rec_score_thresh</span></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Umbral de confianza de reconocimiento</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Configuración de Ray Tune</span></h4>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>tuner = tune.Tuner(
trainable_paddle_ocr,
tune_config=tune.TuneConfig(
metric="CER",
mode="min",
search_alg=OptunaSearch(),
num_samples=64,
max_concurrent_trials=2
),
run_config=air.RunConfig(verbose=2, log_to_file=False),
param_space=search_space
)</pre></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Métrica objetivo</b>: CER (minimizar)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Algoritmo de búsqueda</b>: Optuna (TPE - Tree-structured Parzen Estimator)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Número de trials</b>: 64</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Trials concurrentes</b>: 2</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.2.3. Resultados de la Optimización</span></h3>
<h4><span lang="ES">Estadísticas Descriptivas</span></h4>
<p class="MsoNormal"><span lang="ES">Del archivo CSV de resultados (<span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">raytune_paddle_subproc_results_20251207_192320.csv</span>):</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 13.</b> <i>Tabla 7. Estadísticas descriptivas de los 64 trials de Ray Tune.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Estadística</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">CER</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">WER</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Tiempo (s)</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Tiempo/Página (s)</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>count</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">64</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">64</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">64</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">64</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>mean</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">5.25%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">14.28%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">347.61</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">69.42</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>std</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">11.03%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">10.75%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">7.88</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">1.57</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>min</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">1.15%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">9.89%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">320.97</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">64.10</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>25%</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">1.20%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">10.04%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">34
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Mejor Configuración Encontrada</span></h4>
<p class="MsoNormal"><span lang="ES">Según el análisis del notebook:</span></p>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>Best CER: 0.011535 (1.15%)
Best WER: 0.098902 (9.89%)
Configuración óptima:
textline_orientation: True
use_doc_orientation_classify: False
use_doc_unwarping: False
text_det_thresh: 0.4690
text_det_box_thresh: 0.5412
text_det_unclip_ratio: 0.0
text_rec_score_thresh: 0.6350</pre></span></p>
<h4><span lang="ES">Análisis de Correlación</span></h4>
<p class="MsoNormal"><span lang="ES">Correlación de Pearson entre parámetros y métricas de error (del notebook):</span></p>
<p class="MsoNormal"><span lang="ES"><b>Correlación con CER:</b></span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 14.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Parámetro</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Correlación</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">CER</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">1.000</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">config/text_det_box_thresh</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">0.226</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">config/text_rec_score_thresh</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">-0.161</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>config/text_det_thresh</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>-0.523</b></span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">config/text_det_unclip_ratio</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">NaN</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="ES"><b>Correlación con WER:</b></span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 15.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Parámetro</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Correlación</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">WER</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">1.000</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">config/text_det_box_thresh</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">0.227</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">config/text_rec_score_thresh</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">-0.173</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>config/text_det_thresh</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>-0.521</b></span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">config/text_det_unclip_ratio</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">NaN</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="ES"><b>Hallazgo clave</b>: El parámetro <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span> muestra la correlación más fuerte (-0.52), indicando que valores más altos de este umbral tienden a reducir el error.</span></p>
<h4><span lang="ES">Impacto del Parámetro textline_orientation</span></h4>
<p class="MsoNormal"><span lang="ES">Según el análisis del notebook, este parámetro booleano tiene el mayor impacto:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 16.</b> <i>Tabla 8. Impacto del parámetro textline_orientation en las métricas de error.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">textline_orientation</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">CER Medio</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">WER Medio</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">True</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">~3.76%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">~12.73%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">False</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">~12.40%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">~21.71%</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="ES"><b>Interpretación</b>: El CER medio es ~3.3x menor con <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">textline_orientation=True</span> (3.76% vs 12.40%). Además, la varianza es mucho menor, lo que indica resultados más consistentes. Para documentos en español con layouts mixtos (tablas, encabezados, direcciones), la clasificación de orientación ayuda a PaddleOCR a ordenar correctamente las líneas de texto.</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES"><b>Figura 6.</b> <i>Impacto de textline_orientation en CER</i></span></p>
<p class="MsoNormal" style="text-align:center"><span lang="ES"><img alt="Impacto de textline_orientation en CER" src="figures/figura_6.png" width="500"/></span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Análisis de Fallos</span></h4>
<p class="MsoNormal"><span lang="ES">Los trials con CER muy alto (&gt;40%) se produjeron cuando:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span> &lt; 0.1 (valores muy bajos)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">textline_orientation = False</span></span></p>
<p class="MsoNormal"><span lang="ES">Ejemplo de trial con fallo catastrófico:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">CER: 51.61%</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">WER: 59.45%</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Configuración: <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh=0.017</span>, <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">textline_orientation=True</span></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.2.4. Comparación Baseline vs Optimizado</span></h3>
<h4><span lang="ES">Resultados sobre Dataset Completo (24 páginas)</span></h4>
<p class="MsoNormal"><span lang="ES">Del análisis final del notebook ejecutando sobre las 24 páginas:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 17.</b> <i>Tabla 9. Comparación baseline vs configuración optimizada (24 páginas).</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Modelo</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">CER</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">WER</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PaddleOCR (Baseline)</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">7.78%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">14.94%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PaddleOCR-HyperAdjust</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">1.49%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">7.62%</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Métricas de Mejora</span></h4>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 18.</b> <i>Tabla 10. Análisis de la mejora obtenida.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Métrica</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Baseline</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Optimizado</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Mejora Absoluta</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Reducción Error</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">CER</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">7.78%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">1.49%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">-6.29 pp</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">80.9%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">WER</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">14.94%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">7.62%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">-7.32 pp</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">49.0%</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Interpretación (del notebook)</span></h4>
<p class="MsoNormal" style="margin-left:2cm;margin-right:1cm"><i><span lang="ES">"La optimización de hiperparámetros mejoró la precisión de caracteres de 92.2% a 98.5%, una ganancia de 6.3 puntos porcentuales. Aunque el baseline ya ofrecía resultados aceptables, la configuración optimizada reduce los errores residuales en un 80.9%."</span></i></p>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES"><b>Figura 7.</b> <i>Comparación Baseline vs Optimizado (24 páginas)</i></span></p>
<p class="MsoNormal" style="text-align:center"><span lang="ES"><img alt="Comparación Baseline vs Optimizado (24 páginas)" src="figures/figura_7.png" width="500"/></span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm;text-align:center"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="ES"><b>Impacto práctico</b>: En un documento de 10,000 caracteres:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Baseline: ~778 caracteres con error</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Optimizado: ~149 caracteres con error</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Diferencia: ~629 caracteres menos con errores</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.2.5. Tiempo de Ejecución</span></h3>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 19.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Métrica</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Valor</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Tiempo total del experimento</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">~6 horas (64 trials × ~6 min/trial)</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Tiempo medio por trial</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">367.72 segundos</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Tiempo medio por página</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">69.42 segundos</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Total páginas procesadas</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">64 trials × 5 páginas = 320 evaluaciones</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.2.6. Resumen de la Sección</span></h3>
<p class="MsoNormal"><span lang="ES">Esta sección ha presentado:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Configuración del experimento</b>: 64 trials con Ray Tune + Optuna sobre 7 hiperparámetros</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">2.<span style="font-size:7pt">   </span><b>Resultados estadísticos</b>: CER medio 5.25%, CER mínimo 1.15%</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">3.<span style="font-size:7pt">   </span><b>Hallazgos clave</b>:</span></p>
<p class="MsoNormal"><span lang="ES"> - <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">textline_orientation=True</span> es crítico (reduce CER ~70%) - <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span> tiene correlación -0.52 con CER - Valores bajos de <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span> (&lt;0.1) causan fallos catastróficos</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Mejora final</b>: CER reducido de 7.78% a 1.49% (reducción del 80.9%)</span></p>
<p class="MsoNormal"><span lang="ES"><b>Fuentes de datos:</b></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/paddle_ocr_fine_tune_unir_raytune.ipynb</span>: Código del experimento</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/raytune_paddle_subproc_results_20251207_192320.csv</span>: Resultados de 64 trials</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/paddle_ocr_tuning.py</span>: Script de evaluación</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">4.3. Discusión y análisis de resultados</span></h2>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.3.1. Introducción</span></h3>
<p class="MsoNormal"><span lang="ES">Esta sección presenta un análisis consolidado de los resultados obtenidos en las fases de benchmark comparativo y optimización de hiperparámetros. Se discuten las implicaciones prácticas y se evalúa el cumplimiento de los objetivos planteados.</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.3.2. Resumen de Resultados</span></h3>
<h4><span lang="ES">Resultados del Benchmark Comparativo</span></h4>
<p class="MsoNormal"><span lang="ES">Del archivo <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">results/ai_ocr_benchmark_finetune_results_20251206_113206.csv</span>, PaddleOCR con configuración inicial (<span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_textline_orientation=True</span>) obtuvo los siguientes resultados en las páginas 5-9:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 20.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Página</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">WER</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">CER</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">5</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">12.16%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">6.33%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">6</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">12.81%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">6.40%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">7</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">11.06%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">6.24%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">8</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">8.13%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">1.54%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">9</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">10.61%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">5.58%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>Promedio</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>10.95%</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>5.22%</b></span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Resultados de la Optimización con Ray Tune</span></h4>
<p class="MsoNormal"><span lang="ES">Del archivo <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/raytune_paddle_subproc_results_20251207_192320.csv</span> (64 trials):</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 21.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Métrica</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Valor</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">CER mínimo</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">1.15%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">CER medio</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">5.25%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">CER máximo</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">51.61%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">WER mínimo</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">9.89%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">WER medio</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">14.28%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">WER máximo</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">59.45%</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Comparación Final (Dataset Completo - 24 páginas)</span></h4>
<p class="MsoNormal"><span lang="ES">Resultados del notebook <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/paddle_ocr_fine_tune_unir_raytune.ipynb</span>:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 22.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Modelo</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">CER</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Precisión Caracteres</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">WER</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Precisión Palabras</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PaddleOCR (Baseline)</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">7.78%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">92.22%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">14.94%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">85.06%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PaddleOCR-HyperAdjust</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">1.49%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">98.51%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">7.62%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">92.38%</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.3.3. Análisis de Resultados</span></h3>
<h4><span lang="ES">Mejora Obtenida</span></h4>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 23.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Forma de Medición</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Valor</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Mejora en precisión de caracteres (absoluta)</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">+6.29 puntos porcentuales</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Reducción del CER (relativa)</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">80.9%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Mejora en precisión de palabras (absoluta)</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">+7.32 puntos porcentuales</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Reducción del WER (relativa)</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">49.0%</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Precisión final de caracteres</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">98.51%</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Impacto de Hiperparámetros Individuales</span></h4>
<p class="MsoNormal"><span lang="ES"><b>Parámetro <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">textline_orientation</span></b></span></p>
<p class="MsoNormal"><span lang="ES">Este parámetro booleano demostró ser el más influyente:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 24.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Valor</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">CER Medio</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Impacto</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">True</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">~3.76%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Rendimiento óptimo</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">False</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">~12.40%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">3.3x peor</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="ES"><b>Reducción del CER</b>: 69.7% cuando se habilita la clasificación de orientación de línea.</span></p>
<p class="MsoNormal"><span lang="ES"><b>Parámetro <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span></b></span></p>
<p class="MsoNormal"><span lang="ES">Correlación con CER: <b>-0.523</b> (la más fuerte de los parámetros continuos)</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 25.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Rango</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Comportamiento</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">&lt; 0.1</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Fallos catastróficos (CER 40-50%)</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">0.3 - 0.6</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Rendimiento óptimo</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Valor óptimo</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">0.4690</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="ES"><b>Parámetros con menor impacto</b></span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 26.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Parámetro</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Correlación con CER</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Valor óptimo</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">text_det_box_thresh</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">+0.226</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">0.5412</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">text_rec_score_thresh</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">-0.161</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">0.6350</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">use_doc_orientation_classify</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">-</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">False</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">use_doc_unwarping</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">-</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">False</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Configuración Óptima Final</span></h4>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>config_optimizada = {
"textline_orientation": True, # CRÍTICO
"use_doc_orientation_classify": False,
"use_doc_unwarping": False,
"text_det_thresh": 0.4690, # Correlación -0.52
"text_det_box_thresh": 0.5412,
"text_det_unclip_ratio": 0.0,
"text_rec_score_thresh": 0.6350,
}</pre></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.3.4. Discusión</span></h3>
<h4><span lang="ES">Hallazgos Principales</span></h4>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Importancia de la clasificación de orientación de línea</b>: El parámetro <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">textline_orientation=True</span> es el factor más determinante. Esto tiene sentido para documentos con layouts mixtos (tablas, encabezados, direcciones) donde el orden correcto de las líneas de texto es crucial.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Umbral de detección crítico</b>: El parámetro <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span> presenta un umbral mínimo efectivo (~0.1). Valores inferiores generan demasiados falsos positivos en la detección, corrompiendo el reconocimiento posterior.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Componentes opcionales innecesarios</b>: Para documentos académicos digitales (no escaneados), los módulos de corrección de orientación de documento (<span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_orientation_classify</span>) y corrección de deformación (<span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_unwarping</span>) no aportan mejora e incluso pueden introducir overhead.</span></p>
<h4><span lang="ES">Interpretación de la Correlación Negativa</span></h4>
<p class="MsoNormal"><span lang="ES">La correlación negativa de <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span> (-0.52) con el CER indica que:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Umbrales más altos filtran detecciones de baja confianza</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Esto reduce falsos positivos que generan texto erróneo</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">El reconocimiento es más preciso con menos regiones pero más confiables</span></p>
<h4><span lang="ES">Limitaciones de los Resultados</span></h4>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Generalización</b>: Los resultados se obtuvieron sobre documentos de un único tipo (instrucciones académicas UNIR). La configuración óptima puede variar para otros tipos de documentos.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Ground truth automático</b>: El texto de referencia se extrajo programáticamente del PDF. En layouts complejos, esto puede introducir errores en la evaluación.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Ejecución en CPU</b>: Los tiempos reportados (~69s/página) corresponden a ejecución en CPU. Con GPU, los tiempos serían significativamente menores.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Parámetro fijo</b>: <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_unclip_ratio</span> permaneció fijo en 0.0 durante todo el experimento por decisión de diseño.</span></p>
<h4><span lang="ES">Comparación con Objetivos</span></h4>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 27.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Objetivo</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Meta</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Resultado</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Cumplimiento</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">OE1: Comparar soluciones OCR</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Evaluar EasyOCR, PaddleOCR, DocTR</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PaddleOCR seleccionado</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">✓</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">OE2: Preparar dataset</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Construir dataset estructurado</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Dataset de 24 páginas</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">✓</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">OE3: Identificar hiperparámetros críticos</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Analizar correlaciones</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">textline_orientation</span> y <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span> identificados</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">✓</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">OE4: Optimizar con Ray Tune</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Mínimo 50 configuraciones</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">64 trials ejecutados</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">✓</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">OE5: Validar configuración</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Documentar mejora</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">CER 7.78% → 1.49%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">✓</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>Objetivo General</b></span></p></td><td style="border:solid windowtext 1.0pt;padding
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.3.5. Implicaciones Prácticas</span></h3>
<h4><span lang="ES">Recomendaciones de Configuración</span></h4>
<p class="MsoNormal"><span lang="ES">Para documentos académicos en español similares a los evaluados:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Obligatorio</b>: <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_textline_orientation=True</span></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">2.<span style="font-size:7pt">   </span><b>Recomendado</b>: <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span> entre 0.4 y 0.5</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">3.<span style="font-size:7pt">   </span><b>Opcional</b>: <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_box_thresh</span> ~0.5, <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_rec_score_thresh</span> &gt;0.6</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">4.<span style="font-size:7pt">   </span><b>No recomendado</b>: Habilitar <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_orientation_classify</span> o <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_unwarping</span> para documentos digitales</span></p>
<h4><span lang="ES">Impacto Cuantitativo</span></h4>
<p class="MsoNormal"><span lang="ES">En un documento típico de 10,000 caracteres:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 28.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Configuración</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Errores estimados</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Baseline</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">~778 caracteres</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Optimizada</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">~149 caracteres</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>Reducción</b></span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>629 caracteres menos con errores</b></span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h4><span lang="ES">Aplicabilidad</span></h4>
<p class="MsoNormal"><span lang="ES">Esta metodología de optimización es aplicable cuando:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">No se dispone de recursos GPU para fine-tuning</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">El modelo preentrenado ya tiene soporte para el idioma objetivo</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Se busca mejorar rendimiento sin reentrenar</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">4.3.6. Resumen de la Sección</span></h3>
<p class="MsoNormal"><span lang="ES">Esta sección ha presentado:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span>Los resultados consolidados del benchmark y la optimización</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">2.<span style="font-size:7pt">   </span>El análisis del impacto de cada hiperparámetro</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">3.<span style="font-size:7pt">   </span>La configuración óptima identificada</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">4.<span style="font-size:7pt">   </span>La discusión de limitaciones y aplicabilidad</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">5.<span style="font-size:7pt">   </span>El cumplimiento de los objetivos planteados</span></p>
<p class="MsoNormal"><span lang="ES"><b>Resultado principal</b>: Se logró reducir el CER del 7.78% al 1.49% (mejora del 80.9%) mediante optimización de hiperparámetros, cumpliendo el objetivo de alcanzar CER &lt; 2%.</span></p>
<p class="MsoNormal"><span lang="ES"><b>Fuentes de datos:</b></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">results/ai_ocr_benchmark_finetune_results_20251206_113206.csv</span></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/raytune_paddle_subproc_results_20251207_192320.csv</span></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/paddle_ocr_fine_tune_unir_raytune.ipynb</span></span></p><h1 style="mso-list:l22 level1 lfo18"><a name="_Toc160619818"><?if !supportLists?><span lang="ES" style='mso-fareast-font-family:"Calibri Light";mso-fareast-theme-font:
major-latin;mso-bidi-font-family:"Calibri Light";mso-bidi-theme-font:major-latin'><span style="mso-list:Ignore">5.<span style='font:7.0pt "Times New Roman"'>  
</span></span></span><?endif?><span lang="ES" style="text-transform:none">Conclusiones
y trabajo futuro</span></a></h1><p class="MsoNormal"><span lang="ES">Este capítulo resume las principales conclusiones del trabajo, evalúa el grado de cumplimiento de los objetivos planteados y propone líneas de trabajo futuro que permitirían ampliar y profundizar los resultados obtenidos.</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">5.1. Conclusiones</span></h2>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">5.1.1. Conclusiones Generales</span></h3>
<p class="MsoNormal"><span lang="ES">Este Trabajo Fin de Máster ha demostrado que es posible mejorar significativamente el rendimiento de sistemas OCR preentrenados mediante optimización sistemática de hiperparámetros, sin requerir fine-tuning ni recursos GPU dedicados.</span></p>
<p class="MsoNormal"><span lang="ES">El objetivo principal del trabajo era alcanzar un CER inferior al 2% en documentos académicos en español. Los resultados obtenidos confirman el cumplimiento de este objetivo:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 29.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Métrica</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Objetivo</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Resultado</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">CER</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">&lt; 2%</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES"><b>1.49%</b></span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">5.1.2. Conclusiones Específicas</span></h3>
<p class="MsoNormal"><span lang="ES"><b>Respecto a OE1 (Comparativa de soluciones OCR)</b>:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Se evaluaron tres soluciones OCR de código abierto: EasyOCR, PaddleOCR (PP-OCRv5) y DocTR</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">PaddleOCR demostró el mejor rendimiento base para documentos en español</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">La configurabilidad del pipeline de PaddleOCR lo hace idóneo para optimización</span></p>
<p class="MsoNormal"><span lang="ES"><b>Respecto a OE2 (Preparación del dataset)</b>:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Se construyó un dataset estructurado con 24 páginas de documentos académicos</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">La clase <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">ImageTextDataset</span> facilita la carga de pares imagen-texto</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">El ground truth se extrajo automáticamente del PDF mediante PyMuPDF</span></p>
<p class="MsoNormal"><span lang="ES"><b>Respecto a OE3 (Identificación de hiperparámetros críticos)</b>:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">El parámetro <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">textline_orientation</span> es el más influyente: reduce el CER en un 69.7% cuando está habilitado</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">El umbral <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span> presenta la correlación más fuerte (-0.52) con el CER</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Los parámetros de corrección de documento (<span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_orientation_classify</span>, <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">use_doc_unwarping</span>) no aportan mejora en documentos digitales</span></p>
<p class="MsoNormal"><span lang="ES"><b>Respecto a OE4 (Optimización con Ray Tune)</b>:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Se ejecutaron 64 trials con el algoritmo OptunaSearch</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">El tiempo total del experimento fue aproximadamente 6 horas (en CPU)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">La arquitectura basada en subprocesos permitió superar incompatibilidades entre Ray y PaddleOCR</span></p>
<p class="MsoNormal"><span lang="ES"><b>Respecto a OE5 (Validación de la configuración)</b>:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">Se validó la configuración óptima sobre el dataset completo de 24 páginas</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">La mejora obtenida fue del 80.9% en reducción del CER (7.78% → 1.49%)</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES">La precisión de caracteres alcanzó el 98.51%</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">5.1.3. Hallazgos Clave</span></h3>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Arquitectura sobre umbrales</b>: Un único parámetro booleano (<span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">textline_orientation</span>) tiene más impacto que todos los umbrales continuos combinados.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Umbrales mínimos efectivos</b>: Valores de <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_thresh</span> &lt; 0.1 causan fallos catastróficos (CER &gt;40%).</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Simplicidad para documentos digitales</b>: Para documentos PDF digitales (no escaneados), los módulos de corrección de orientación y deformación son innecesarios.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Optimización sin fine-tuning</b>: Se puede mejorar significativamente el rendimiento de modelos preentrenados mediante ajuste de hiperparámetros de inferencia.</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">5.1.4. Contribuciones del Trabajo</span></h3>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Metodología reproducible</b>: Se documenta un proceso completo de optimización de hiperparámetros OCR con Ray Tune + Optuna.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Análisis de hiperparámetros de PaddleOCR</b>: Se cuantifica el impacto de cada parámetro configurable mediante correlaciones y análisis comparativo.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Configuración óptima para español</b>: Se proporciona una configuración validada para documentos académicos en español.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Código fuente</b>: Todo el código está disponible en el repositorio GitHub para reproducción y extensión.</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">5.1.5. Limitaciones del Trabajo</span></h3>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Tipo de documento único</b>: Los experimentos se realizaron únicamente sobre documentos académicos de UNIR. La generalización a otros tipos de documentos requiere validación adicional.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Tamaño del dataset</b>: 24 páginas es un corpus limitado para conclusiones estadísticamente robustas.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Ground truth automático</b>: La extracción automática del texto de referencia puede introducir errores en layouts complejos.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Ejecución en CPU</b>: Los tiempos de procesamiento (~69s/página) limitan la aplicabilidad en escenarios de alto volumen.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Parámetro no explorado</b>: <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_unclip_ratio</span> permaneció fijo en 0.0 durante todo el experimento.</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">5.2. Líneas de trabajo futuro</span></h2>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">5.2.1. Extensiones Inmediatas</span></h3>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Validación cruzada</b>: Evaluar la configuración óptima en otros tipos de documentos en español (facturas, formularios, textos manuscritos).</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Exploración de <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">text_det_unclip_ratio</span></b>: Incluir este parámetro en el espacio de búsqueda.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Dataset ampliado</b>: Construir un corpus más amplio y diverso de documentos en español.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Evaluación con GPU</b>: Medir tiempos de inferencia con aceleración GPU.</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">5.2.2. Líneas de Investigación</span></h3>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Transfer learning de hiperparámetros</b>: Investigar si las configuraciones óptimas para un tipo de documento transfieren a otros dominios.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Optimización multi-objetivo</b>: Considerar simultáneamente CER, WER y tiempo de inferencia como objetivos.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>AutoML para OCR</b>: Aplicar técnicas de AutoML más avanzadas (Neural Architecture Search, meta-learning).</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Comparación con fine-tuning</b>: Cuantificar la brecha de rendimiento entre optimización de hiperparámetros y fine-tuning real.</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">5.2.3. Aplicaciones Prácticas</span></h3>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Herramienta de configuración automática</b>: Desarrollar una herramienta que determine automáticamente la configuración óptima para un nuevo tipo de documento.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Integración en pipelines de producción</b>: Implementar la configuración optimizada en sistemas reales de procesamiento documental.</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span><b>Benchmark público</b>: Publicar un benchmark de OCR para documentos en español que facilite la comparación de soluciones.</span></p>
<h3 style="mso-list:l22 level3 lfo18"><span lang="ES" style="text-transform:none">5.2.4. Reflexión Final</span></h3>
<p class="MsoNormal"><span lang="ES">Este trabajo demuestra que, en un contexto de recursos limitados donde el fine-tuning de modelos de deep learning no es viable, la optimización de hiperparámetros representa una alternativa práctica y efectiva para mejorar sistemas OCR.</span></p>
<p class="MsoNormal"><span lang="ES">La metodología propuesta es reproducible, los resultados son cuantificables, y las conclusiones son aplicables a escenarios reales de procesamiento documental. La reducción del CER del 7.78% al 1.49% representa una mejora sustancial que puede tener impacto directo en aplicaciones downstream como extracción de información, análisis semántico y búsqueda de documentos.</span></p>
<p class="MsoNormal"><span lang="ES">El código fuente y los datos experimentales están disponibles públicamente para facilitar la reproducción y extensión de este trabajo.</span></p><p class="Ttulo1sinnumerar"><a name="_Toc160619821"><span lang="ES">Referencias
bibliográficas</span></a></p><p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Akiba, T., Sano, S., Yanase, T., Ohta, T., &amp; Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. <i>Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining</i>, 2623-2631. https://doi.org/10.1145/3292500.3330701</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Baek, Y., Lee, B., Han, D., Yun, S., &amp; Lee, H. (2019). Character region awareness for text detection. <i>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, 9365-9374. https://doi.org/10.1109/CVPR.2019.00959</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Bergstra, J., &amp; Bengio, Y. (2012). Random search for hyper-parameter optimization. <i>Journal of Machine Learning Research</i>, 13(1), 281-305. https://jmlr.org/papers/v13/bergstra12a.html</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Bergstra, J., Bardenet, R., Bengio, Y., &amp; Kégl, B. (2011). Algorithms for hyper-parameter optimization. <i>Advances in Neural Information Processing Systems</i>, 24, 2546-2554. https://papers.nips.cc/paper/2011/hash/86e8f7ab32cfd12577bc2619bc635690-Abstract.html</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Cohen, J. (1988). <i>Statistical power analysis for the behavioral sciences</i> (2nd ed.). Lawrence Erlbaum Associates.</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Doran, G. T. (1981). There's a S.M.A.R.T. way to write management's goals and objectives. <i>Management Review</i>, 70(11), 35-36.</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Du, Y., Li, C., Guo, R., Yin, X., Liu, W., Zhou, J., Bai, Y., Yu, Z., Yang, Y., Dang, Q., &amp; Wang, H. (2020). PP-OCR: A practical ultra lightweight OCR system. <i>arXiv preprint arXiv:2009.09941</i>. https://arxiv.org/abs/2009.09941</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Du, Y., Li, C., Guo, R., Cui, C., Liu, W., Zhou, J., Lu, B., Yang, Y., Liu, Q., Hu, X., Yu, D., &amp; Wang, H. (2023). PP-OCRv4: Mobile scene text detection and recognition. <i>arXiv preprint arXiv:2310.05930</i>. https://arxiv.org/abs/2310.05930</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Feurer, M., &amp; Hutter, F. (2019). Hyperparameter optimization. In F. Hutter, L. Kotthoff, &amp; J. Vanschoren (Eds.), <i>Automated machine learning: Methods, systems, challenges</i> (pp. 3-33). Springer. https://doi.org/10.1007/978-3-030-05318-5_1</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">He, P., Huang, W., Qiao, Y., Loy, C. C., &amp; Tang, X. (2016). Reading scene text in deep convolutional sequences. <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, 30(1), 3501-3508. https://doi.org/10.1609/aaai.v30i1.10291</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">JaidedAI. (2020). EasyOCR: Ready-to-use OCR with 80+ supported languages. GitHub. https://github.com/JaidedAI/EasyOCR</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Liang, J., Doermann, D., &amp; Li, H. (2005). Camera-based analysis of text and documents: A survey. <i>International Journal of Document Analysis and Recognition</i>, 7(2), 84-104. https://doi.org/10.1007/s10032-004-0138-z</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Liao, M., Wan, Z., Yao, C., Chen, K., &amp; Bai, X. (2020). Real-time scene text detection with differentiable binarization. <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, 34(07), 11474-11481. https://doi.org/10.1609/aaai.v34i07.6812</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J. E., &amp; Stoica, I. (2018). Tune: A research platform for distributed model selection and training. <i>arXiv preprint arXiv:1807.05118</i>. https://arxiv.org/abs/1807.05118</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Mindee. (2021). DocTR: Document Text Recognition. GitHub. https://github.com/mindee/doctr</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., Elibol, M., Yang, Z., Paul, W., Jordan, M. I., &amp; Stoica, I. (2018). Ray: A distributed framework for emerging AI applications. <i>13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)</i>, 561-577. https://www.usenix.org/conference/osdi18/presentation/moritz</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Morris, A. C., Maier, V., &amp; Green, P. D. (2004). From WER and RIL to MER and WIL: Improved evaluation measures for connected speech recognition. <i>Eighth International Conference on Spoken Language Processing</i>. https://doi.org/10.21437/Interspeech.2004-668</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">PaddlePaddle. (2024). PaddleOCR: Awesome multilingual OCR toolkits based on PaddlePaddle. GitHub. https://github.com/PaddlePaddle/PaddleOCR</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Pearson, K. (1895). Notes on regression and inheritance in the case of two parents. <i>Proceedings of the Royal Society of London</i>, 58, 240-242. https://doi.org/10.1098/rspl.1895.0041</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">PyMuPDF. (2024). PyMuPDF documentation. https://pymupdf.readthedocs.io/</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Shi, B., Bai, X., &amp; Yao, C. (2016). An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 39(11), 2298-2304. https://doi.org/10.1109/TPAMI.2016.2646371</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Smith, R. (2007). An overview of the Tesseract OCR engine. <i>Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)</i>, 2, 629-633. https://doi.org/10.1109/ICDAR.2007.4376991</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., &amp; Liang, J. (2017). EAST: An efficient and accurate scene text detector. <i>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition</i>, 5551-5560. https://doi.org/10.1109/CVPR.2017.283</span></p>
<p class="MsoBibliography" style="margin-left:36.0pt;text-indent:-36.0pt"><span lang="ES">Zoph, B., &amp; Le, Q. V. (2017). Neural architecture search with reinforcement learning. <i>International Conference on Learning Representations (ICLR)</i>. https://arxiv.org/abs/1611.01578</span></p>
<p class="Anexo" style="mso-list:l2 level1 lfo23"><a name="_Toc160619822"><?if !supportLists?><span lang="ES" style='mso-fareast-font-family:"Calibri Light";mso-fareast-theme-font:
major-latin;mso-bidi-font-family:"Calibri Light";mso-bidi-theme-font:major-latin'><span style="mso-list:Ignore">Anexo A.<span style='font:7.0pt "Times New Roman"'>   
</span></span></span><?endif?><span lang="ES">Código fuente y datos analizados</span></a></p><h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">A.1 Repositorio del Proyecto</span></h2>
<p class="MsoNormal"><span lang="ES">El código fuente completo y los datos utilizados en este trabajo están disponibles en el siguiente repositorio:</span></p>
<p class="MsoNormal"><span lang="ES"><b>URL del repositorio:</b> https://github.com/seryus/MastersThesis</span></p>
<p class="MsoNormal"><span lang="ES">El repositorio incluye:</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Notebooks de experimentación</b>: Código completo de los experimentos realizados</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Scripts de evaluación</b>: Herramientas para evaluar modelos OCR</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Dataset</b>: Imágenes y textos de referencia utilizados</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES" style="font-family:Symbol">·</span><span lang="ES" style="font-size:7pt">     </span><span lang="ES"><b>Resultados</b>: Archivos CSV con los resultados de los 64 trials de Ray Tune</span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">A.2 Estructura del Repositorio</span></h2>
<p class="MsoNormal" style="margin-left:1cm;background:#F5F5F5;padding:10px"><span style="font-family:Consolas;font-size:9pt"><pre>MastersThesis/
├── docs/ # Capítulos de la tesis en Markdown
├── src/
│ ├── paddle_ocr_fine_tune_unir_raytune.ipynb # Experimento principal
│ ├── paddle_ocr_tuning.py # Script de evaluación CLI
│ ├── dataset_manager.py # Clase ImageTextDataset
│ ├── prepare_dataset.ipynb # Preparación del dataset
│ └── raytune_paddle_subproc_results_*.csv # Resultados de 64 trials
├── results/ # Resultados de benchmarks
├── instructions/ # Instrucciones y plantilla UNIR
└── README.md</pre></span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">A.3 Requisitos de Software</span></h2>
<p class="MsoNormal"><span lang="ES">Para reproducir los experimentos se requieren las siguientes dependencias:</span></p>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES"><b>Tabla 30.</b> <i>Tabla de datos.</i></span></p>
<table border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse:collapse;border:none;mso-border-alt:solid windowtext .5pt"><tr><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Componente</span></b></p></td><td style="border:solid windowtext 1.0pt;padding:5px;background:#F0F0F0"><p class="MsoNormal" style="margin:0"><b><span lang="ES">Versión</span></b></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Python</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">3.11.9</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PaddlePaddle</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">3.2.2</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PaddleOCR</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">3.3.2</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Ray</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">2.52.1</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">Optuna</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">4.6.0</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">jiwer</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">(última versión)</span></p></td></tr><tr><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">PyMuPDF</span></p></td><td style="border:solid windowtext 1.0pt;padding:5px"><p class="MsoNormal" style="margin:0"><span lang="ES">(última versión)</span></p></td></tr></table>
<p class="Piedefoto-tabla" style="margin-left:0cm"><span lang="ES">Fuente: Elaboración propia.</span></p>
<p class="MsoNormal"><span lang="ES"><o:p> </o:p></span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">A.4 Instrucciones de Ejecución</span></h2>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">1.<span style="font-size:7pt">   </span>Clonar el repositorio</span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">2.<span style="font-size:7pt">   </span>Instalar dependencias: <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">pip install -r requirements.txt</span></span></p>
<p class="MsoListParagraphCxSpMiddle" style="margin-left:36pt;text-indent:-18pt"><span lang="ES">3.<span style="font-size:7pt">   </span>Ejecutar el notebook <span style="font-family:Consolas;font-size:10pt;background:#f5f5f5">src/paddle_ocr_fine_tune_unir_raytune.ipynb</span></span></p>
<h2 style="mso-list:l22 level2 lfo18"><span lang="ES" style="text-transform:none">A.5 Licencia</span></h2>
<p class="MsoNormal"><span lang="ES">El código se distribuye bajo licencia MIT.</span></p></div>
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