From 647a8d1c7bc1e4667fd1981e8c0644ee65ac1690 Mon Sep 17 00:00:00 2001 From: sergio Date: Mon, 15 Dec 2025 23:54:41 +0100 Subject: [PATCH] generation test --- .gitignore | 3 +- TFM_Sergio_Jimenez_OCR_Optimization.docx | Bin 106754 -> 0 bytes apply_content.py | 571 ++ generate_mermaid_figures.py | 110 + generate_thesis.py | 269 - generate_thesis_docx.py | 438 -- package-lock.json | 4127 +++++++++++++ package.json | 5 + thesis_output.htm | 751 --- thesis_output/figures/figura_1.png | Bin 0 -> 17100 bytes thesis_output/figures/figura_2.png | Bin 0 -> 55971 bytes thesis_output/figures/figura_3.png | Bin 0 -> 98404 bytes thesis_output/figures/figura_4.png | Bin 0 -> 32850 bytes thesis_output/figures/figura_5.png | Bin 0 -> 24564 bytes thesis_output/figures/figura_6.png | Bin 0 -> 16914 bytes thesis_output/figures/figura_7.png | Bin 0 -> 17910 bytes thesis_output/figures/figures_manifest.json | 37 + thesis_output/plantilla_individual.htm | 5321 +++++++++++++++++ .../colorschememapping.xml | 0 .../plantilla_individual_files}/filelist.xml | 0 .../plantilla_individual_files}/header.htm | 0 .../plantilla_individual_files}/image001.png | Bin .../plantilla_individual_files}/image002.gif | Bin .../plantilla_individual_files}/image003.png | Bin .../plantilla_individual_files}/image004.jpg | Bin .../plantilla_individual_files}/image005.png | Bin .../plantilla_individual_files}/image006.gif | Bin .../plantilla_individual_files}/item0001.xml | 0 .../plantilla_individual_files}/item0003.xml | 0 .../plantilla_individual_files}/item0005.xml | 0 .../plantilla_individual_files}/item0007.xml | 0 .../plantilla_individual_files}/props002.xml | 0 .../plantilla_individual_files}/props004.xml | 0 .../plantilla_individual_files}/props006.xml | 0 .../plantilla_individual_files}/props008.xml | 0 .../themedata.thmx | Bin 36 files changed, 10173 insertions(+), 1459 deletions(-) delete mode 100644 TFM_Sergio_Jimenez_OCR_Optimization.docx create mode 100644 apply_content.py create mode 100644 generate_mermaid_figures.py delete mode 100644 generate_thesis.py delete mode 100644 generate_thesis_docx.py create mode 100644 package-lock.json create mode 100644 package.json delete mode 100644 thesis_output.htm create mode 100644 thesis_output/figures/figura_1.png create mode 100644 thesis_output/figures/figura_2.png create mode 100644 thesis_output/figures/figura_3.png create mode 100644 thesis_output/figures/figura_4.png create mode 100644 thesis_output/figures/figura_5.png create mode 100644 thesis_output/figures/figura_6.png create mode 100644 thesis_output/figures/figura_7.png create mode 100644 thesis_output/figures/figures_manifest.json create mode 100644 thesis_output/plantilla_individual.htm rename {thesis_output_files => thesis_output/plantilla_individual_files}/colorschememapping.xml (100%) rename {thesis_output_files => thesis_output/plantilla_individual_files}/filelist.xml (100%) rename {thesis_output_files => thesis_output/plantilla_individual_files}/header.htm (100%) rename {thesis_output_files => thesis_output/plantilla_individual_files}/image001.png (100%) rename {thesis_output_files => thesis_output/plantilla_individual_files}/image002.gif (100%) rename {thesis_output_files => thesis_output/plantilla_individual_files}/image003.png (100%) rename {thesis_output_files => thesis_output/plantilla_individual_files}/image004.jpg (100%) rename {thesis_output_files => thesis_output/plantilla_individual_files}/image005.png (100%) rename {thesis_output_files => thesis_output/plantilla_individual_files}/image006.gif (100%) rename {thesis_output_files => thesis_output/plantilla_individual_files}/item0001.xml (100%) rename {thesis_output_files => thesis_output/plantilla_individual_files}/item0003.xml (100%) rename {thesis_output_files => thesis_output/plantilla_individual_files}/item0005.xml (100%) rename {thesis_output_files => thesis_output/plantilla_individual_files}/item0007.xml (100%) rename {thesis_output_files => 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zb8~=Ju8AoE@g#E_w&TJYRWOy{1Cs-S{e>k(o+c^9Bf)VtOArL)iF`JZwT2I&Lmf#rguIEGhQbQ<2!a4!Jd(-VGj6Qfv16_4h?za)a7$8`gMhYG# z!046h{&foe7;aSL)k=<85mUIIy2xu|7dUTR^S6e54iN8$k4eE%5DJ!{cGB zA51n{PoF>jpxC=vbkM*q>CNbqJp$-2#p6diJf*1%N6YsKpfSRH-W5SpsOGdCN0L52gss`~HvsdQ;xK4r@cGp$ev-9vD%ucrw!s z3msWQ6JWmpEINRI^@sQKM5nxsw^};h&Dv1Aha>oH34(Y_kOCnkvO2na6h^`*Hj7<1 iqG$#qxibBH6f4qKCkF|zT_6YvKHq_Fg;&AsLjMH;RV4WU diff --git a/apply_content.py b/apply_content.py new file mode 100644 index 0000000..b5d332e --- /dev/null +++ b/apply_content.py @@ -0,0 +1,571 @@ +#!/usr/bin/env python3 +"""Replace template content with thesis content from docs/ folder using BeautifulSoup.""" + +import re +import os +from bs4 import BeautifulSoup, NavigableString + +BASE_DIR = '/Users/sergio/Desktop/MastersThesis' +TEMPLATE = os.path.join(BASE_DIR, 'thesis_output/plantilla_individual.htm') +DOCS_DIR = os.path.join(BASE_DIR, 'docs') + +# Global counters for tables and figures +table_counter = 0 +figure_counter = 0 + +def read_file(path): + try: + with open(path, 'r', encoding='utf-8') as f: + return f.read() + except UnicodeDecodeError: + with open(path, 'r', encoding='latin-1') as f: + return f.read() + +def write_file(path, content): + with open(path, 'w', encoding='utf-8') as f: + f.write(content) + +def md_to_html_para(text): + """Convert markdown inline formatting to HTML.""" + # Bold + text = re.sub(r'\*\*([^*]+)\*\*', r'\1', text) + # Italic + text = re.sub(r'\*([^*]+)\*', r'\1', text) + # Inline code + text = re.sub(r'`([^`]+)`', r'\1', text) + return text + +def extract_table_title(lines, current_index): + """Look for table title in preceding lines (e.g., **Tabla 1.** *Title*).""" + # Check previous non-empty lines for table title + for i in range(current_index - 1, max(0, current_index - 5), -1): + line = lines[i].strip() + if line.startswith('**Tabla') or line.startswith('*Tabla'): + return line + if line and not line.startswith('|'): + break + return None + +def extract_figure_title_from_mermaid(lines, current_index): + """Extract title from mermaid diagram or preceding text.""" + # Look for title in mermaid content + for i in range(current_index + 1, min(len(lines), current_index + 20)): + line = lines[i].strip() + if line.startswith('```'): + break + if 'title' in line.lower(): + # Extract title from: title "Some Title" + match = re.search(r'title\s+["\']([^"\']+)["\']', line) + if match: + return match.group(1) + + # Check preceding lines for figure reference + for i in range(current_index - 1, max(0, current_index - 3), -1): + line = lines[i].strip() + if line.startswith('**Figura') or 'Figura' in line: + return line + + return None + +def parse_md_to_html_blocks(md_content): + """Convert markdown content to HTML blocks with template styles.""" + global table_counter, figure_counter + + html_blocks = [] + lines = md_content.split('\n') + i = 0 + + while i < len(lines): + line = lines[i] + + # Skip empty lines + if not line.strip(): + i += 1 + continue + + # Mermaid diagram - convert to figure with actual image + if line.strip().startswith('```mermaid'): + figure_counter += 1 + mermaid_lines = [] + i += 1 + while i < len(lines) and not lines[i].strip() == '```': + mermaid_lines.append(lines[i]) + i += 1 + + # Try to extract title from mermaid content + mermaid_content = '\n'.join(mermaid_lines) + title_match = re.search(r'title\s+["\']?([^"\'"\n]+)["\']?', mermaid_content) + if title_match: + fig_title = title_match.group(1).strip() + else: + fig_title = f"Diagrama {figure_counter}" + + # Check if the generated PNG exists + fig_file = f'figures/figura_{figure_counter}.png' + fig_path = os.path.join(BASE_DIR, 'thesis_output', fig_file) + + # Create figure with proper template format (Piedefoto-tabla class) + html_blocks.append(f'''

Figura {figure_counter}. {fig_title}

''') + + if os.path.exists(fig_path): + # Use actual image with proper Word-compatible format + html_blocks.append(f'''

{fig_title}

''') + else: + # Fallback to placeholder + html_blocks.append(f'''

[Insertar diagrama Mermaid aquí]

''') + + html_blocks.append(f'''

Fuente: Elaboración propia.

''') + html_blocks.append('

 

') + i += 1 + continue + + # Code block (non-mermaid) + if line.strip().startswith('```'): + code_lang = line.strip()[3:] + code_lines = [] + i += 1 + while i < len(lines) and not lines[i].strip().startswith('```'): + code_lines.append(lines[i]) + i += 1 + code = '\n'.join(code_lines) + # Escape HTML entities in code + code = code.replace('&', '&').replace('<', '<').replace('>', '>') + html_blocks.append(f'

{code}

') + i += 1 + continue + + # Headers - ## becomes h2, ### becomes h3 + if line.startswith('####'): + text = line.lstrip('#').strip() + html_blocks.append(f'

{text}

') + i += 1 + continue + elif line.startswith('###'): + text = line.lstrip('#').strip() + html_blocks.append(f'

{text}

') + i += 1 + continue + elif line.startswith('##'): + text = line.lstrip('#').strip() + html_blocks.append(f'

{text}

') + i += 1 + continue + elif line.startswith('#'): + # Skip h1 - we keep the original + i += 1 + continue + + # Table - check for table title pattern first + if '|' in line and i + 1 < len(lines) and '---' in lines[i + 1]: + table_counter += 1 + + # Check if previous line has table title (e.g., **Tabla 1.** *Title*) + table_title = None + table_source = "Elaboración propia" + + # Look back for table title + for j in range(i - 1, max(0, i - 5), -1): + prev_line = lines[j].strip() + if prev_line.startswith('**Tabla') or prev_line.startswith('*Tabla'): + # Extract title text + table_title = re.sub(r'\*+', '', prev_line).strip() + break + elif prev_line and not prev_line.startswith('|'): + break + + # Parse table + table_lines = [] + while i < len(lines) and '|' in lines[i]: + if '---' not in lines[i]: + table_lines.append(lines[i]) + i += 1 + + # Look ahead for source + if i < len(lines) and 'Fuente:' in lines[i]: + table_source = lines[i].replace('*', '').replace('Fuente:', '').strip() + i += 1 + + # Add table title with proper template format (Piedefoto-tabla class) + if table_title: + clean_title = table_title.replace(f"Tabla {table_counter}.", "").strip() + html_blocks.append(f'

Tabla {table_counter}. {clean_title}

') + else: + html_blocks.append(f'

Tabla {table_counter}. Tabla de datos.

') + + # Build table HTML + table_html = '' + for j, tline in enumerate(table_lines): + cells = [c.strip() for c in tline.split('|')[1:-1]] + table_html += '' + for cell in cells: + if j == 0: + # Header row + table_html += f'' + else: + table_html += f'' + table_html += '' + table_html += '

{md_to_html_para(cell)}

{md_to_html_para(cell)}

' + html_blocks.append(table_html) + + # Add source with proper template format + html_blocks.append(f'

Fuente: {table_source}.

') + html_blocks.append('

 

') + continue + + # Blockquote + if line.startswith('>'): + quote_text = line[1:].strip() + i += 1 + while i < len(lines) and lines[i].startswith('>'): + quote_text += ' ' + lines[i][1:].strip() + i += 1 + html_blocks.append(f'

{md_to_html_para(quote_text)}

') + continue + + # Bullet list + if re.match(r'^[\-\*\+]\s', line): + while i < len(lines) and re.match(r'^[\-\*\+]\s', lines[i]): + item_text = lines[i][2:].strip() + html_blocks.append(f'

·     {md_to_html_para(item_text)}

') + i += 1 + continue + + # Numbered list + if re.match(r'^\d+\.\s', line): + num = 1 + while i < len(lines) and re.match(r'^\d+\.\s', lines[i]): + item_text = re.sub(r'^\d+\.\s*', '', lines[i]).strip() + html_blocks.append(f'

{num}.   {md_to_html_para(item_text)}

') + num += 1 + i += 1 + continue + + # Skip lines that are just table/figure titles (they'll be handled with the table/figure) + if line.strip().startswith('**Tabla') or line.strip().startswith('*Tabla'): + i += 1 + continue + if line.strip().startswith('**Figura') or line.strip().startswith('*Figura'): + i += 1 + continue + if line.strip().startswith('*Fuente:') or line.strip().startswith('Fuente:'): + i += 1 + continue + + # Regular paragraph + para_lines = [line] + i += 1 + while i < len(lines) and lines[i].strip() and not lines[i].startswith('#') and not lines[i].startswith('```') and not lines[i].startswith('>') and not re.match(r'^[\-\*\+]\s', lines[i]) and not re.match(r'^\d+\.\s', lines[i]) and '|' not in lines[i]: + para_lines.append(lines[i]) + i += 1 + + para_text = ' '.join(para_lines) + html_blocks.append(f'

{md_to_html_para(para_text)}

') + + return '\n\n'.join(html_blocks) + +def extract_section_content(md_content): + """Extract content from markdown, skipping the first # header.""" + md_content = re.sub(r'^#\s+[^\n]+\n+', '', md_content, count=1) + return parse_md_to_html_blocks(md_content) + +def find_section_element(soup, keyword): + """Find element containing keyword (h1 or special paragraph classes).""" + # First try h1 + for h1 in soup.find_all('h1'): + text = h1.get_text() + if keyword.lower() in text.lower(): + return h1 + + # Try special paragraph classes for unnumbered sections + for p in soup.find_all('p', class_=['Ttulo1sinnumerar', 'Anexo', 'MsoNormal']): + text = p.get_text() + if keyword.lower() in text.lower(): + classes = p.get('class', []) + if 'Ttulo1sinnumerar' in classes or 'Anexo' in classes: + return p + if re.match(r'^\d+\.?\s', text.strip()): + return p + return None + +def remove_elements_between(start_elem, end_elem): + """Remove all elements between start and end (exclusive).""" + current = start_elem.next_sibling + elements_to_remove = [] + while current and current != end_elem: + elements_to_remove.append(current) + current = current.next_sibling + for elem in elements_to_remove: + if hasattr(elem, 'decompose'): + elem.decompose() + elif isinstance(elem, NavigableString): + elem.extract() + +def format_references(refs_content): + """Format references with proper MsoBibliography style.""" + refs_content = refs_content.replace('# Referencias bibliográficas {.unnumbered}', '').strip() + refs_html = '' + + for line in refs_content.split('\n\n'): + line = line.strip() + if not line: + continue + + # Apply markdown formatting + formatted = md_to_html_para(line) + + # Use MsoBibliography style with hanging indent (36pt indent, -36pt text-indent) + refs_html += f'''

{formatted}

\n''' + + return refs_html + +def extract_resumen_parts(resumen_content): + """Extract Spanish resumen and English abstract from 00_resumen.md""" + parts = resumen_content.split('---') + + spanish_part = parts[0] if len(parts) > 0 else '' + english_part = parts[1] if len(parts) > 1 else '' + + # Extract Spanish content + spanish_text = '' + spanish_keywords = '' + if '**Palabras clave:**' in spanish_part: + text_part, kw_part = spanish_part.split('**Palabras clave:**') + spanish_text = text_part.replace('# Resumen', '').strip() + spanish_keywords = kw_part.strip() + else: + spanish_text = spanish_part.replace('# Resumen', '').strip() + + # Extract English content + english_text = '' + english_keywords = '' + if '**Keywords:**' in english_part: + text_part, kw_part = english_part.split('**Keywords:**') + english_text = text_part.replace('# Abstract', '').strip() + english_keywords = kw_part.strip() + else: + english_text = english_part.replace('# Abstract', '').strip() + + return spanish_text, spanish_keywords, english_text, english_keywords + +def main(): + global table_counter, figure_counter + + print("Reading template...") + html_content = read_file(TEMPLATE) + soup = BeautifulSoup(html_content, 'html.parser') + + print("Reading docs content...") + docs = { + 'resumen': read_file(os.path.join(DOCS_DIR, '00_resumen.md')), + 'intro': read_file(os.path.join(DOCS_DIR, '01_introduccion.md')), + 'contexto': read_file(os.path.join(DOCS_DIR, '02_contexto_estado_arte.md')), + 'objetivos': read_file(os.path.join(DOCS_DIR, '03_objetivos_metodologia.md')), + 'desarrollo': read_file(os.path.join(DOCS_DIR, '04_desarrollo_especifico.md')), + 'conclusiones': read_file(os.path.join(DOCS_DIR, '05_conclusiones_trabajo_futuro.md')), + 'referencias': read_file(os.path.join(DOCS_DIR, '06_referencias_bibliograficas.md')), + 'anexo': read_file(os.path.join(DOCS_DIR, '07_anexo_a.md')), + } + + # Extract resumen and abstract + spanish_text, spanish_kw, english_text, english_kw = extract_resumen_parts(docs['resumen']) + + # Replace title + print("Replacing title...") + for elem in soup.find_all(string=re.compile(r'Título del TFE', re.IGNORECASE)): + elem.replace_with(elem.replace('Título del TFE', 'Optimización de Hiperparámetros OCR con Ray Tune para Documentos Académicos en Español')) + + # Replace Resumen section + print("Replacing Resumen...") + resumen_title = soup.find('p', class_='Ttulondices', string=re.compile(r'Resumen')) + if resumen_title: + # Find and replace content after Resumen title until Abstract + current = resumen_title.find_next_sibling() + elements_to_remove = [] + while current: + text = current.get_text() if hasattr(current, 'get_text') else str(current) + if 'Abstract' in text and current.name == 'p' and 'Ttulondices' in str(current.get('class', [])): + break + elements_to_remove.append(current) + current = current.find_next_sibling() + + for elem in elements_to_remove: + if hasattr(elem, 'decompose'): + elem.decompose() + + # Insert new resumen content + resumen_html = f'''

{spanish_text}

+

 

+

Palabras clave: {spanish_kw}

+

 

''' + resumen_soup = BeautifulSoup(resumen_html, 'html.parser') + insert_point = resumen_title + for new_elem in reversed(list(resumen_soup.children)): + insert_point.insert_after(new_elem) + print(" ✓ Replaced Resumen") + + # Replace Abstract section + print("Replacing Abstract...") + abstract_title = soup.find('p', class_='Ttulondices', string=re.compile(r'Abstract')) + if abstract_title: + # Find and replace content after Abstract title until next major section + current = abstract_title.find_next_sibling() + elements_to_remove = [] + while current: + # Stop at page break or next title + if current.name == 'span' and 'page-break' in str(current): + break + text = current.get_text() if hasattr(current, 'get_text') else str(current) + if current.name == 'p' and ('Ttulondices' in str(current.get('class', [])) or 'MsoToc' in str(current.get('class', []))): + break + elements_to_remove.append(current) + current = current.find_next_sibling() + + for elem in elements_to_remove: + if hasattr(elem, 'decompose'): + elem.decompose() + + # Insert new abstract content + abstract_html = f'''

{english_text}

+

 

+

Keywords: {english_kw}

+

 

''' + abstract_soup = BeautifulSoup(abstract_html, 'html.parser') + insert_point = abstract_title + for new_elem in reversed(list(abstract_soup.children)): + insert_point.insert_after(new_elem) + print(" ✓ Replaced Abstract") + + # Remove "Importante" callout boxes (template instructions) + print("Removing template instructions...") + for div in soup.find_all('div'): + text = div.get_text() + if 'Importante:' in text and 'extensión mínima' in text: + div.decompose() + print(" ✓ Removed 'Importante' box") + + # Remove "Ejemplo de nota al pie" footnote + for elem in soup.find_all(string=re.compile(r'Ejemplo de nota al pie')): + parent = elem.parent + if parent: + # Find the footnote container and remove it + while parent and parent.name != 'p': + parent = parent.parent + if parent: + parent.decompose() + print(" ✓ Removed footnote example") + + # Clear old figure/table index entries (they need to be regenerated in Word) + print("Clearing old index entries...") + # Remove old figure index entries that reference template examples + for p in soup.find_all('p', class_='MsoToc3'): + text = p.get_text() + if 'Figura 1. Ejemplo' in text or 'Tabla 1. Ejemplo' in text: + p.decompose() + print(" ✓ Removed template index entry") + + # Also clear the specific figure/table from template + for p in soup.find_all('p', class_='Imagencentrada'): + p.decompose() + print(" ✓ Removed template figure placeholder") + + # Remove template table example + for table in soup.find_all('table', class_='MsoTableGrid'): + # Check if this is the template example table + text = table.get_text() + if 'Celda 1' in text or 'Encabezado 1' in text: + # Also remove surrounding caption and source + prev_sib = table.find_previous_sibling() + next_sib = table.find_next_sibling() + if prev_sib and 'Tabla 1. Ejemplo' in prev_sib.get_text(): + prev_sib.decompose() + if next_sib and 'Fuente:' in next_sib.get_text(): + next_sib.decompose() + table.decompose() + print(" ✓ Removed template table example") + break + + # Define chapters with their keywords and next chapter keywords + chapters = [ + ('Introducción', 'intro', 'Contexto'), + ('Contexto', 'contexto', 'Objetivos'), + ('Objetivos', 'objetivos', 'Desarrollo'), + ('Desarrollo', 'desarrollo', 'Conclusiones'), + ('Conclusiones', 'conclusiones', 'Referencias'), + ] + + print("Replacing chapter contents...") + for chapter_keyword, doc_key, next_keyword in chapters: + print(f" Processing: {chapter_keyword}") + + # Reset counters for consistent numbering per chapter (optional - remove if you want global numbering) + # table_counter = 0 + # figure_counter = 0 + + start_elem = find_section_element(soup, chapter_keyword) + end_elem = find_section_element(soup, next_keyword) + + if start_elem and end_elem: + remove_elements_between(start_elem, end_elem) + new_content_html = extract_section_content(docs[doc_key]) + new_soup = BeautifulSoup(new_content_html, 'html.parser') + insert_point = start_elem + for new_elem in reversed(list(new_soup.children)): + insert_point.insert_after(new_elem) + print(f" ✓ Replaced content") + else: + if not start_elem: + print(f" Warning: Could not find start element for {chapter_keyword}") + if not end_elem: + print(f" Warning: Could not find end element for {next_keyword}") + + # Handle Referencias + print(" Processing: Referencias bibliográficas") + refs_start = find_section_element(soup, 'Referencias') + anexo_elem = find_section_element(soup, 'Anexo') + + if refs_start and anexo_elem: + remove_elements_between(refs_start, anexo_elem) + refs_html = format_references(docs['referencias']) + refs_soup = BeautifulSoup(refs_html, 'html.parser') + insert_point = refs_start + for new_elem in reversed(list(refs_soup.children)): + insert_point.insert_after(new_elem) + print(f" ✓ Replaced content") + + # Handle Anexo (last section) + print(" Processing: Anexo") + if anexo_elem: + body = soup.find('body') + if body: + current = anexo_elem.next_sibling + while current: + next_elem = current.next_sibling + if hasattr(current, 'decompose'): + current.decompose() + elif isinstance(current, NavigableString): + current.extract() + current = next_elem + + anexo_content = extract_section_content(docs['anexo']) + anexo_soup = BeautifulSoup(anexo_content, 'html.parser') + insert_point = anexo_elem + for new_elem in reversed(list(anexo_soup.children)): + insert_point.insert_after(new_elem) + print(f" ✓ Replaced content") + + print(f"\nSummary: {table_counter} tables, {figure_counter} figures processed") + + print("Saving modified template...") + output_html = str(soup) + write_file(TEMPLATE, output_html) + + print(f"✓ Done! Modified: {TEMPLATE}") + print("\nTo convert to DOCX:") + print("1. Open the .htm file in Microsoft Word") + print("2. Replace [Insertar diagrama Mermaid aquí] placeholders with actual diagrams") + print("3. Update indices: Select all (Ctrl+A) then press F9 to update fields") + print(" - This will regenerate: Índice de contenidos, Índice de figuras, Índice de tablas") + print("4. Save as .docx") + +if __name__ == '__main__': + main() diff --git a/generate_mermaid_figures.py b/generate_mermaid_figures.py new file mode 100644 index 0000000..a2e5ce7 --- /dev/null +++ b/generate_mermaid_figures.py @@ -0,0 +1,110 @@ +#!/usr/bin/env python3 +"""Extract Mermaid diagrams from markdown files and convert to PNG images.""" + +import os +import re +import subprocess +import json + +BASE_DIR = '/Users/sergio/Desktop/MastersThesis' +DOCS_DIR = os.path.join(BASE_DIR, 'docs') +OUTPUT_DIR = os.path.join(BASE_DIR, 'thesis_output/figures') +MMDC = os.path.join(BASE_DIR, 'node_modules/.bin/mmdc') + +def extract_mermaid_diagrams(): + """Extract all mermaid diagrams from markdown files.""" + diagrams = [] + + md_files = [ + '02_contexto_estado_arte.md', + '03_objetivos_metodologia.md', + '04_desarrollo_especifico.md', + ] + + for md_file in md_files: + filepath = os.path.join(DOCS_DIR, md_file) + if not os.path.exists(filepath): + continue + + with open(filepath, 'r', encoding='utf-8') as f: + content = f.read() + + # Find all mermaid blocks + pattern = r'```mermaid\n(.*?)```' + matches = re.findall(pattern, content, re.DOTALL) + + for i, mermaid_code in enumerate(matches): + # Try to extract title + title_match = re.search(r'title\s+["\']?([^"\'"\n]+)["\']?', mermaid_code) + title = title_match.group(1).strip() if title_match else f"Diagrama de {md_file}" + + diagrams.append({ + 'source': md_file, + 'code': mermaid_code.strip(), + 'title': title, + 'index': len(diagrams) + 1 + }) + + return diagrams + +def convert_to_png(diagrams): + """Convert mermaid diagrams to PNG using mmdc.""" + os.makedirs(OUTPUT_DIR, exist_ok=True) + + generated = [] + + for diagram in diagrams: + # Write mermaid code to temp file + temp_file = os.path.join(OUTPUT_DIR, f'temp_{diagram["index"]}.mmd') + output_file = os.path.join(OUTPUT_DIR, f'figura_{diagram["index"]}.png') + + with open(temp_file, 'w', encoding='utf-8') as f: + f.write(diagram['code']) + + # Convert using mmdc + try: + result = subprocess.run( + [MMDC, '-i', temp_file, '-o', output_file, '-b', 'white', '-w', '800'], + capture_output=True, + text=True, + timeout=60 + ) + + if os.path.exists(output_file): + print(f"✓ Generated: figura_{diagram['index']}.png - {diagram['title']}") + generated.append({ + 'file': f'figura_{diagram["index"]}.png', + 'title': diagram['title'], + 'index': diagram['index'] + }) + else: + print(f"✗ Failed: figura_{diagram['index']}.png - {result.stderr}") + except subprocess.TimeoutExpired: + print(f"✗ Timeout: figura_{diagram['index']}.png") + except Exception as e: + print(f"✗ Error: figura_{diagram['index']}.png - {e}") + + # Clean up temp file + if os.path.exists(temp_file): + os.remove(temp_file) + + return generated + +def main(): + print("Extracting Mermaid diagrams from markdown files...") + diagrams = extract_mermaid_diagrams() + print(f"Found {len(diagrams)} diagrams\n") + + print("Converting to PNG images...") + generated = convert_to_png(diagrams) + + print(f"\n✓ Generated {len(generated)} figures in {OUTPUT_DIR}") + + # Save manifest for apply_content.py to use + manifest_file = os.path.join(OUTPUT_DIR, 'figures_manifest.json') + with open(manifest_file, 'w', encoding='utf-8') as f: + json.dump(generated, f, indent=2, ensure_ascii=False) + print(f"✓ Saved manifest to {manifest_file}") + +if __name__ == '__main__': + main() diff --git a/generate_thesis.py b/generate_thesis.py deleted file mode 100644 index bf9103c..0000000 --- a/generate_thesis.py +++ /dev/null @@ -1,269 +0,0 @@ -#!/usr/bin/env python3 -"""Generate thesis DOCX from HTML template and markdown content.""" - -import os -import re -import shutil -import subprocess -from bs4 import BeautifulSoup - -BASE_DIR = '/Users/sergio/Desktop/MastersThesis' -TEMPLATE_HTM = os.path.join(BASE_DIR, 'instructions/plantilla_individual.htm') -TEMPLATE_FILES = os.path.join(BASE_DIR, 'instructions/plantilla_individual_files') -OUTPUT_HTM = os.path.join(BASE_DIR, 'thesis_output.htm') -OUTPUT_FILES = os.path.join(BASE_DIR, 'thesis_output_files') -OUTPUT_DOCX = os.path.join(BASE_DIR, 'TFM_Sergio_Jimenez_OCR_Optimization.docx') -DOCS_DIR = os.path.join(BASE_DIR, 'docs') - -def read_md(filename): - with open(os.path.join(DOCS_DIR, filename), 'r', encoding='utf-8') as f: - return f.read() - -def md_to_html(md_text): - """Convert markdown to simple HTML.""" - html = md_text - - # Headers - html = re.sub(r'^#### (.+)$', r'

\1

', html, flags=re.MULTILINE) - html = re.sub(r'^### (.+)$', r'

\1

', html, flags=re.MULTILINE) - html = re.sub(r'^## (.+)$', r'

\1

', html, flags=re.MULTILINE) - html = re.sub(r'^# (.+)$', r'

\1

', html, flags=re.MULTILINE) - - # Bold and italic - html = re.sub(r'\*\*([^*]+)\*\*', r'\1', html) - html = re.sub(r'\*([^*]+)\*', r'\1', html) - - # Inline code - html = re.sub(r'`([^`]+)`', r'\1', html) - - # Code blocks - def code_block_replace(match): - lang = match.group(1) - code = match.group(2) - return f'
{code}
' - html = re.sub(r'```(\w*)\n(.*?)```', code_block_replace, html, flags=re.DOTALL) - - # Blockquotes - html = re.sub(r'^>\s*(.+)$', r'
\1
', html, flags=re.MULTILINE) - - # Tables - def table_replace(match): - lines = match.group(0).strip().split('\n') - rows = [] - for line in lines: - if '---' in line: - continue - cells = [c.strip() for c in line.split('|')[1:-1]] - rows.append(cells) - - table_html = '' - for i, row in enumerate(rows): - table_html += '' - tag = 'th' if i == 0 else 'td' - for cell in row: - table_html += f'<{tag} style="padding:5px;border:1px solid #ccc">{cell}' - table_html += '' - table_html += '
' - return table_html - - html = re.sub(r'(\|[^\n]+\|\n)+', table_replace, html) - - # Bullet lists - def bullet_list_replace(match): - items = match.group(0).strip().split('\n') - list_html = '
    ' - for item in items: - item_text = re.sub(r'^[\-\*\+]\s*', '', item) - list_html += f'
  • {item_text}
    • ' - list_html += '
' - return list_html - html = re.sub(r'(^[\-\*\+]\s+.+\n?)+', bullet_list_replace, html, flags=re.MULTILINE) - - # Numbered lists - def num_list_replace(match): - items = match.group(0).strip().split('\n') - list_html = '
    ' - for item in items: - item_text = re.sub(r'^\d+\.\s*', '', item) - list_html += f'
  1. {item_text}
    2. ' - list_html += '
' - return list_html - html = re.sub(r'(^\d+\.\s+.+\n?)+', num_list_replace, html, flags=re.MULTILINE) - - # Paragraphs (lines not already in tags) - lines = html.split('\n') - result = [] - for line in lines: - line = line.strip() - if not line: - continue - if line.startswith('<') or line.startswith('{'): - result.append(line) - else: - result.append(f'

{line}

') - - return '\n'.join(result) - -def main(): - print("Reading template...") - with open(TEMPLATE_HTM, 'r', encoding='utf-8', errors='ignore') as f: - html = f.read() - - soup = BeautifulSoup(html, 'html.parser') - - # Read markdown files - print("Reading markdown content...") - md_files = { - 'resumen': read_md('00_resumen.md'), - 'intro': read_md('01_introduccion.md'), - 'contexto': read_md('02_contexto_estado_arte.md'), - 'objetivos': read_md('03_objetivos_metodologia.md'), - 'desarrollo': read_md('04_desarrollo_especifico.md'), - 'conclusiones': read_md('05_conclusiones_trabajo_futuro.md'), - 'referencias': read_md('06_referencias_bibliograficas.md'), - 'anexo': read_md('07_anexo_a.md'), - } - - # Convert markdown to HTML - print("Converting markdown to HTML...") - html_content = {} - for key, md in md_files.items(): - html_content[key] = md_to_html(md) - - # Find and replace content sections - print("Replacing template content...") - - # Find all WordSection divs and main content areas - sections = soup.find_all('div', class_=lambda x: x and 'WordSection' in x) - - # Strategy: Find chapter headings and replace following content - # The template has placeholders we need to replace - - # Simple approach: Create new HTML with template structure but our content - new_html = ''' - - - -TFM - Optimización de Hiperparámetros OCR - - - -''' - - # Title page - new_html += ''' -
-

UNIR Logo

-

Universidad Internacional de La Rioja
Escuela Superior de Ingeniería y Tecnología

-

Máster Universitario en Inteligencia Artificial

-

Optimización de Hiperparámetros OCR con Ray Tune para Documentos Académicos en Español

-

Trabajo Fin de Estudio presentado por: Sergio Jiménez Jiménez

-

Tipo de trabajo: Comparativa de soluciones / Piloto experimental

-

Director: [Nombre del Director]

-

Fecha: 2025

-
-''' - - # Resumen - new_html += '
\n' - new_html += html_content['resumen'] - new_html += '
\n' - - # Table of contents placeholder - new_html += ''' -
-

Índice de contenidos

-

[El índice se generará automáticamente en Word]

-
-''' - - # Chapters - chapters = [ - ('intro', 'introduccion'), - ('contexto', 'contexto'), - ('objetivos', 'objetivos'), - ('desarrollo', 'desarrollo'), - ('conclusiones', 'conclusiones'), - ] - - for key, _ in chapters: - new_html += '
\n' - new_html += html_content[key] - new_html += '
\n' - - # Referencias - new_html += '
\n' - new_html += html_content['referencias'] - new_html += '
\n' - - # Anexo - new_html += '
\n' - new_html += html_content['anexo'] - new_html += '
\n' - - new_html += '' - - # Save HTML - print(f"Saving HTML to {OUTPUT_HTM}...") - with open(OUTPUT_HTM, 'w', encoding='utf-8') as f: - f.write(new_html) - - # Copy template files folder - if os.path.exists(OUTPUT_FILES): - shutil.rmtree(OUTPUT_FILES) - if os.path.exists(TEMPLATE_FILES): - shutil.copytree(TEMPLATE_FILES, OUTPUT_FILES) - - # Create UNIR logo placeholder if not exists - os.makedirs(OUTPUT_FILES, exist_ok=True) - - # Convert to DOCX using pandoc - print(f"Converting to DOCX with pandoc...") - result = subprocess.run([ - 'pandoc', - OUTPUT_HTM, - '-o', OUTPUT_DOCX, - '--reference-doc', os.path.join(BASE_DIR, 'instructions/plantilla_individual.docx'), - '--toc', - '--toc-depth=3' - ], capture_output=True, text=True) - - if result.returncode != 0: - print(f"Pandoc error: {result.stderr}") - # Try without reference doc - print("Retrying without reference doc...") - result = subprocess.run([ - 'pandoc', - OUTPUT_HTM, - '-o', OUTPUT_DOCX, - '--toc', - '--toc-depth=3' - ], capture_output=True, text=True) - - if result.returncode == 0: - print(f"✓ Document saved to {OUTPUT_DOCX}") - print(f"✓ HTML version saved to {OUTPUT_HTM}") - else: - print(f"Error: {result.stderr}") - -if __name__ == '__main__': - main() diff --git a/generate_thesis_docx.py b/generate_thesis_docx.py deleted file mode 100644 index 32194f0..0000000 --- a/generate_thesis_docx.py +++ /dev/null @@ -1,438 +0,0 @@ -#!/usr/bin/env python3 -""" -Generate thesis.docx from markdown files using UNIR template. -""" - -import re -import os -from docx import Document -from docx.shared import Pt, Cm, RGBColor, Inches -from docx.enum.text import WD_ALIGN_PARAGRAPH -from docx.enum.style import WD_STYLE_TYPE -from docx.oxml.ns import qn -from docx.oxml import OxmlElement - -# Paths -TEMPLATE_PATH = 'instructions/plantilla_individual.docx' -OUTPUT_PATH = 'TFM_Sergio_Jimenez_OCR_Optimization.docx' -DOCS_PATH = 'docs' - -# Thesis metadata -THESIS_TITLE = "Optimización de Hiperparámetros OCR con Ray Tune para Documentos Académicos en Español" -AUTHOR = "Sergio Jiménez Jiménez" -DIRECTOR = "[Nombre del Director]" -DATE = "2025" - - -def read_markdown_file(filepath): - """Read markdown file and return content.""" - with open(filepath, 'r', encoding='utf-8') as f: - return f.read() - - -def parse_markdown_blocks(md_content): - """Parse markdown content into blocks (headers, paragraphs, code, tables, lists).""" - blocks = [] - lines = md_content.split('\n') - i = 0 - - while i < len(lines): - line = lines[i] - - # Skip empty lines - if not line.strip(): - i += 1 - continue - - # Code block - if line.strip().startswith('```'): - lang = line.strip()[3:] - code_lines = [] - i += 1 - while i < len(lines) and not lines[i].strip().startswith('```'): - code_lines.append(lines[i]) - i += 1 - blocks.append({'type': 'code', 'lang': lang, 'content': '\n'.join(code_lines)}) - i += 1 - continue - - # Headers - if line.startswith('#'): - level = len(line) - len(line.lstrip('#')) - text = line.lstrip('#').strip() - # Remove {.unnumbered} suffix - text = re.sub(r'\s*\{\.unnumbered\}\s*$', '', text) - blocks.append({'type': 'header', 'level': level, 'content': text}) - i += 1 - continue - - # Table - if '|' in line and i + 1 < len(lines) and '---' in lines[i + 1]: - table_lines = [line] - i += 1 - while i < len(lines) and '|' in lines[i]: - table_lines.append(lines[i]) - i += 1 - blocks.append({'type': 'table', 'content': table_lines}) - continue - - # Blockquote - if line.startswith('>'): - quote_text = line[1:].strip() - i += 1 - while i < len(lines) and lines[i].startswith('>'): - quote_text += ' ' + lines[i][1:].strip() - i += 1 - blocks.append({'type': 'quote', 'content': quote_text}) - continue - - # List item (bullet or numbered) - if re.match(r'^[\-\*\+]\s', line) or re.match(r'^\d+\.\s', line): - list_items = [] - list_type = 'numbered' if re.match(r'^\d+\.', line) else 'bullet' - while i < len(lines): - current = lines[i] - if re.match(r'^[\-\*\+]\s', current): - list_items.append(current[2:].strip()) - i += 1 - elif re.match(r'^\d+\.\s', current): - list_items.append(re.sub(r'^\d+\.\s*', '', current).strip()) - i += 1 - elif current.strip() == '': - break - else: - break - blocks.append({'type': 'list', 'list_type': list_type, 'items': list_items}) - continue - - # Figure caption (italic text starting with *Figura or Figura) - if line.strip().startswith('*Figura') or line.strip().startswith('Figura'): - blocks.append({'type': 'caption', 'content': line.strip().strip('*')}) - i += 1 - continue - - # Regular paragraph - para_lines = [line] - i += 1 - while i < len(lines) and lines[i].strip() and not lines[i].startswith('#') and not lines[i].startswith('```') and not lines[i].startswith('>') and not re.match(r'^[\-\*\+]\s', lines[i]) and not re.match(r'^\d+\.\s', lines[i]) and '|' not in lines[i]: - para_lines.append(lines[i]) - i += 1 - - para_text = ' '.join(para_lines) - blocks.append({'type': 'paragraph', 'content': para_text}) - - return blocks - - -def add_formatted_text(paragraph, text): - """Add text with inline formatting (bold, italic, code) to a paragraph.""" - # Pattern for inline formatting - parts = re.split(r'(\*\*[^*]+\*\*|\*[^*]+\*|`[^`]+`)', text) - - for part in parts: - if not part: - continue - if part.startswith('**') and part.endswith('**'): - run = paragraph.add_run(part[2:-2]) - run.bold = True - elif part.startswith('*') and part.endswith('*'): - run = paragraph.add_run(part[1:-1]) - run.italic = True - elif part.startswith('`') and part.endswith('`'): - run = paragraph.add_run(part[1:-1]) - run.font.name = 'Consolas' - run.font.size = Pt(10) - else: - paragraph.add_run(part) - - -def add_table_to_doc(doc, table_lines): - """Add a markdown table to the document.""" - # Parse table - rows = [] - for line in table_lines: - if '---' in line: - continue - cells = [c.strip() for c in line.split('|')[1:-1]] - if cells: - rows.append(cells) - - if not rows: - return - - # Create table - num_cols = len(rows[0]) - table = doc.add_table(rows=len(rows), cols=num_cols) - table.style = 'Table Grid' - - for i, row_data in enumerate(rows): - row = table.rows[i] - for j, cell_text in enumerate(row_data): - if j < len(row.cells): - cell = row.cells[j] - cell.text = '' - para = cell.paragraphs[0] - add_formatted_text(para, cell_text) - if i == 0: # Header row - for run in para.runs: - run.bold = True - - # Add spacing after table - doc.add_paragraph() - - -def add_code_block(doc, code, lang=''): - """Add a code block to the document.""" - para = doc.add_paragraph() - para.paragraph_format.left_indent = Cm(0.5) - para.paragraph_format.space_before = Pt(6) - para.paragraph_format.space_after = Pt(6) - - run = para.add_run(code) - run.font.name = 'Consolas' - run.font.size = Pt(9) - - # Add background shading - shading = OxmlElement('w:shd') - shading.set(qn('w:fill'), 'F5F5F5') - para._p.get_or_add_pPr().append(shading) - - -def get_header_style(level, is_numbered=True): - """Get the appropriate style for a header level.""" - if level == 1: - return 'Heading 1' - elif level == 2: - return 'Heading 2' - elif level == 3: - return 'Heading 3' - elif level == 4: - return 'Heading 4' - else: - return 'Normal' - - -def add_section_content(doc, md_content, start_numbered=True): - """Add markdown content to the document with proper formatting.""" - blocks = parse_markdown_blocks(md_content) - - for block in blocks: - if block['type'] == 'header': - level = block['level'] - text = block['content'] - - # Map markdown header levels to document styles - # ## (level 2) -> Heading 2 (subsection like 1.1. Motivación) - # ### (level 3) -> Heading 3 (sub-subsection like 1.1.1. xxx) - # #### (level 4) -> Heading 4 - - if level == 1: - # Skip level 1 headers - they're added separately as chapter titles - continue - elif level == 2: - para = doc.add_paragraph(text, style='Heading 2') - elif level == 3: - para = doc.add_paragraph(text, style='Heading 3') - elif level == 4: - para = doc.add_paragraph(text, style='Heading 4') - else: - para = doc.add_paragraph(text) - if para.runs: - para.runs[0].bold = True - - elif block['type'] == 'paragraph': - para = doc.add_paragraph() - add_formatted_text(para, block['content']) - - elif block['type'] == 'code': - add_code_block(doc, block['content'], block.get('lang', '')) - - elif block['type'] == 'table': - add_table_to_doc(doc, block['content']) - - elif block['type'] == 'quote': - para = doc.add_paragraph() - para.paragraph_format.left_indent = Cm(1) - para.paragraph_format.right_indent = Cm(1) - add_formatted_text(para, block['content']) - for run in para.runs: - run.italic = True - - elif block['type'] == 'list': - for item in block['items']: - if block['list_type'] == 'bullet': - para = doc.add_paragraph(style='List Paragraph') - para.paragraph_format.left_indent = Cm(1) - add_formatted_text(para, '• ' + item) - else: - para = doc.add_paragraph(style='List Paragraph') - para.paragraph_format.left_indent = Cm(1) - add_formatted_text(para, item) - - elif block['type'] == 'caption': - para = doc.add_paragraph() - para.alignment = WD_ALIGN_PARAGRAPH.CENTER - run = para.add_run(block['content']) - run.italic = True - run.font.size = Pt(10) - - -def create_thesis_document(): - """Create the thesis document from template and markdown files.""" - print("Loading template...") - doc = Document(TEMPLATE_PATH) - - # Find and update title on cover page - for para in doc.paragraphs[:20]: - if 'Título del TFE' in para.text or 'titulo del TFE' in para.text.lower(): - para.clear() - run = para.add_run(THESIS_TITLE) - run.bold = True - - # Clear template content after indices (keep cover, resumen structure) - # We'll find where actual content starts and replace it - - # Read all markdown files - print("Reading markdown files...") - md_files = { - 'resumen': read_markdown_file(os.path.join(DOCS_PATH, '00_resumen.md')), - 'introduccion': read_markdown_file(os.path.join(DOCS_PATH, '01_introduccion.md')), - 'contexto': read_markdown_file(os.path.join(DOCS_PATH, '02_contexto_estado_arte.md')), - 'objetivos': read_markdown_file(os.path.join(DOCS_PATH, '03_objetivos_metodologia.md')), - 'desarrollo': read_markdown_file(os.path.join(DOCS_PATH, '04_desarrollo_especifico.md')), - 'conclusiones': read_markdown_file(os.path.join(DOCS_PATH, '05_conclusiones_trabajo_futuro.md')), - 'referencias': read_markdown_file(os.path.join(DOCS_PATH, '06_referencias_bibliograficas.md')), - 'anexo': read_markdown_file(os.path.join(DOCS_PATH, '07_anexo_a.md')), - } - - # Create new document based on template but with our content - print("Creating new document with thesis content...") - - # Start fresh document with template styles - new_doc = Document(TEMPLATE_PATH) - - # Clear all content after a certain point - # Keep first ~70 paragraphs (cover + resumen structure + indices) - paras_to_remove = [] - found_intro = False - for i, para in enumerate(new_doc.paragraphs): - if 'Introducción' in para.text and para.style and 'Heading 1' in para.style.name: - found_intro = True - if found_intro: - paras_to_remove.append(para) - - # Remove old content - for para in paras_to_remove: - p = para._element - p.getparent().remove(p) - - # Now add our content - print("Adding thesis content...") - - # Add each chapter - chapters = [ - ('introduccion', '1. Introducción'), - ('contexto', '2. Contexto y estado del arte'), - ('objetivos', '3. Objetivos concretos y metodología de trabajo'), - ('desarrollo', '4. Desarrollo específico de la contribución'), - ('conclusiones', '5. Conclusiones y trabajo futuro'), - ] - - for key, title in chapters: - print(f" Adding chapter: {title}") - # Add chapter heading with Heading 1 style - new_doc.add_paragraph(title, style='Heading 1') - - # Remove the top-level header from content (we added it separately with proper style) - content = md_files[key] - # Remove the first # header line and intro paragraph that follows - content = re.sub(r'^#\s+\d+\.\s+[^\n]+\n+', '', content) - add_section_content(new_doc, content) - new_doc.add_page_break() - - # Add Referencias - print(" Adding Referencias bibliográficas") - para = new_doc.add_paragraph('Referencias bibliográficas', style='Título 1 sin numerar') - refs_content = md_files['referencias'] - refs_content = re.sub(r'^#[^\n]+\n+', '', refs_content) # Remove header - - # Parse references (each reference is a paragraph) - for line in refs_content.split('\n\n'): - if line.strip(): - para = new_doc.add_paragraph() - para.paragraph_format.left_indent = Cm(1.27) - para.paragraph_format.first_line_indent = Cm(-1.27) - add_formatted_text(para, line.strip()) - - new_doc.add_page_break() - - # Add Anexo - print(" Adding Anexo A") - para = new_doc.add_paragraph('Anexo A. Código fuente y datos analizados', style='Título 1 sin numerar') - anexo_content = md_files['anexo'] - anexo_content = re.sub(r'^#[^\n]+\n+', '', anexo_content) - add_section_content(new_doc, anexo_content) - - # Update Resumen/Abstract sections (find them in the document and update) - print("Updating Resumen and Abstract...") - resumen_content = md_files['resumen'] - - # Parse resumen file to extract Spanish and English parts - resumen_blocks = parse_markdown_blocks(resumen_content) - spanish_paragraphs = [] - english_paragraphs = [] - keywords_es = "" - keywords_en = "" - current_section = None - - for block in resumen_blocks: - if block['type'] == 'header': - if 'Resumen' in block['content']: - current_section = 'es' - elif 'Abstract' in block['content']: - current_section = 'en' - elif block['type'] == 'paragraph': - text = block['content'] - if 'Palabras clave:' in text: - keywords_es = text - elif 'Keywords:' in text: - keywords_en = text - elif current_section == 'es' and text.strip(): - spanish_paragraphs.append(text) - elif current_section == 'en' and text.strip(): - english_paragraphs.append(text) - - # Find and update Resumen section in doc - found_resumen = False - found_abstract = False - for i, para in enumerate(new_doc.paragraphs): - text = para.text.strip() - - if 'Resumen' in text and para.style and 'Título' in para.style.name: - found_resumen = True - # Update following paragraphs - for j, sp in enumerate(spanish_paragraphs[:3]): # Limit to first 3 paragraphs - if i + j + 1 < len(new_doc.paragraphs): - target_para = new_doc.paragraphs[i + j + 1] - if target_para.style and target_para.style.name == 'Normal': - target_para.clear() - add_formatted_text(target_para, sp) - - elif 'Abstract' in text and para.style and 'Título' in para.style.name: - found_abstract = True - for j, ep in enumerate(english_paragraphs[:3]): - if i + j + 1 < len(new_doc.paragraphs): - target_para = new_doc.paragraphs[i + j + 1] - if target_para.style and target_para.style.name == 'Normal': - target_para.clear() - add_formatted_text(target_para, ep) - - # Save document - print(f"Saving document to {OUTPUT_PATH}...") - new_doc.save(OUTPUT_PATH) - print(f"Done! 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UNIR Logo

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Universidad Internacional de La Rioja
Escuela Superior de Ingeniería y Tecnología

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Máster Universitario en Inteligencia Artificial

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Optimización de Hiperparámetros OCR con Ray Tune para Documentos Académicos en Español

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Trabajo Fin de Estudio presentado por: Sergio Jiménez Jiménez

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Tipo de trabajo: Comparativa de soluciones / Piloto experimental

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Director: [Nombre del Director]

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Fecha: 2025

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Resumen

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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.

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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.

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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.

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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.

-Palabras clave: OCR, Reconocimiento Óptico de Caracteres, PaddleOCR, Optimización de Hiperparámetros, Ray Tune, Procesamiento de Documentos, Inteligencia Artificial -

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Abstract

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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.

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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.

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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.

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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.

-Keywords: OCR, Optical Character Recognition, PaddleOCR, Hyperparameter Optimization, Ray Tune, Document Processing, Artificial Intelligence
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Índice de contenidos

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[El índice se generará automáticamente en Word]

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1. Introducción

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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.

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1.1. Motivación

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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.

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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.

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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.

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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.

-

1.2. Planteamiento del trabajo

-

El problema central que aborda este trabajo puede formularse de la siguiente manera:

-
¿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?
-

Este planteamiento se descompone en las siguientes cuestiones específicas:

-
  1. Selección de modelo base: ¿Cuál de las soluciones OCR de código abierto disponibles (EasyOCR, PaddleOCR, DocTR) ofrece el mejor rendimiento base para documentos en español?
-
  1. Impacto de hiperparámetros: ¿Qué hiperparámetros del pipeline OCR tienen mayor influencia en las métricas de error (CER, WER)?
-
  1. Optimización automatizada: ¿Puede un proceso de búsqueda automatizada de hiperparámetros (mediante Ray Tune/Optuna) encontrar configuraciones que superen significativamente los valores por defecto?
-
  1. Viabilidad práctica: ¿Son los tiempos de inferencia y los recursos requeridos compatibles con un despliegue en entornos con recursos limitados?
-

La relevancia de este problema radica en su aplicabilidad inmediata. Una metodología reproducible para optimizar OCR sin fine-tuning beneficiaría a:

-
  • Investigadores que procesan grandes volúmenes de documentos académicos
  • Instituciones educativas que digitalizan archivos históricos
  • Pequeñas y medianas empresas que automatizan flujos documentales
  • Desarrolladores que integran OCR en aplicaciones con restricciones de recursos
-

1.3. Estructura del trabajo

-

El presente documento se organiza en los siguientes capítulos:

-Capítulo 2 - Contexto y Estado del Arte: 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. -Capítulo 3 - Objetivos y Metodología: 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. -Capítulo 4 - Desarrollo Específico de la Contribución: 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. -Capítulo 5 - Conclusiones y Trabajo Futuro: Se resumen las contribuciones del trabajo, se discute el grado de cumplimiento de los objetivos y se proponen líneas de trabajo futuro. -Anexos: Se incluye el enlace al repositorio de código fuente y datos, así como tablas completas de resultados experimentales.
-
-

2. Contexto y estado del arte

-

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.

-

2.1. Contexto del problema

-

Definición y Evolución Histórica del OCR

-

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:

-
  • Primera generación (1950-1970): Sistemas basados en plantillas que requerían fuentes específicas.
  • Segunda generación (1970-1990): Introducción de técnicas de extracción de características y clasificadores estadísticos.
  • Tercera generación (1990-2010): Modelos basados en Redes Neuronales Artificiales y Modelos Ocultos de Markov (HMM).
  • Cuarta generación (2010-presente): Arquitecturas de aprendizaje profundo que dominan el estado del arte.
-

Pipeline Moderno de OCR

-

Los sistemas OCR modernos siguen típicamente un pipeline de dos etapas:

-

``mermaid

-

flowchart LR

-

subgraph Input

-

A["Imagen de
documento"]

-

end

-

subgraph "Etapa 1: Detección"

-

B["Text Detection
(DB, EAST, CRAFT)"]

-

end

-

subgraph "Etapa 2: Reconocimiento"

-

C["Text Recognition
(CRNN, SVTR, Transformer)"]

-

end

-

subgraph Output

-

D["Texto
extraído"]

-

end

-

A --> B

-

B -->|"Regiones de texto
(bounding boxes)"| C

-

C --> D

-

style A fill:#e1f5fe

-

style D fill:#c8e6c9

-` -Figura 1. Pipeline típico de un sistema OCR moderno con etapas de detección y reconocimiento. -
  1. Detección de texto (Text Detection): Localización de regiones que contienen texto en la imagen. Las arquitecturas más utilizadas incluyen:
- EAST (Efficient and Accurate Scene Text Detector) -

- CRAFT (Character Region Awareness for Text Detection)

-

- DB (Differentiable Binarization)

-
  1. Reconocimiento de texto (Text Recognition): Transcripción del contenido textual de las regiones detectadas. Las arquitecturas predominantes son:
- CRNN (Convolutional Recurrent Neural Network) con CTC loss -

- Arquitecturas encoder-decoder con atención

-

- Transformers (ViTSTR, TrOCR)

-

Métricas de Evaluación

-

Las métricas estándar para evaluar sistemas OCR son:

-Character Error Rate (CER): Se calcula como CER = (S + D + I) / N, donde S = sustituciones, D = eliminaciones, I = inserciones, N = caracteres de referencia. -Word Error Rate (WER): Se calcula de forma análoga pero a nivel de palabras en lugar de caracteres. -

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%.

-

Particularidades del OCR para el Idioma Español

-

El español presenta características específicas que impactan el OCR:

-
  • Caracteres especiales: ñ, á, é, í, ó, ú, ü, ¿, ¡
  • Diacríticos: Los acentos pueden confundirse con ruido o artefactos
  • Longitud de palabras: Palabras generalmente más largas que en inglés
  • Puntuación: Signos de interrogación y exclamación invertidos
-

2.2. Estado del arte

-

Soluciones OCR de Código Abierto

-

EasyOCR

-

EasyOCR es una biblioteca de OCR desarrollada por Jaided AI (2020) que soporta más de 80 idiomas. Sus características principales incluyen:

-
  • Arquitectura: Detector CRAFT + Reconocedor CRNN/Transformer
  • Fortalezas: Facilidad de uso, soporte multilingüe amplio, bajo consumo de memoria
  • Limitaciones: Menor precisión en documentos complejos, opciones de configuración limitadas
  • Caso de uso ideal: Prototipado rápido y aplicaciones con restricciones de memoria
-

PaddleOCR

-

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:

-
  • Arquitectura:
- Detector: DB (Differentiable Binarization) con backbone ResNet (Liao et al., 2020) -

- Reconocedor: SVTR (Scene-Text Visual Transformer Recognition)

-

- Clasificador de orientación opcional

-
  • Hiperparámetros configurables:
-Tabla 1. Hiperparámetros configurables de PaddleOCR. -
ParámetroDescripciónValor por defecto
text_det_threshUmbral de detección de píxeles0.3
text_det_box_threshUmbral de caja de detección0.6
text_det_unclip_ratioCoeficiente de expansión1.5
text_rec_score_threshUmbral de confianza de reconocimiento0.5
use_textline_orientationClasificación de orientaciónFalse
use_doc_orientation_classifyClasificación de orientación de documentoFalse
use_doc_unwarpingCorrección de deformaciónFalse
-Fuente: Documentación oficial de PaddleOCR (PaddlePaddle, 2024). -
  • Fortalezas: Alta precisión, pipeline altamente configurable, modelos específicos para servidor
  • Limitaciones: Mayor complejidad de configuración, dependencia del framework PaddlePaddle
-

DocTR

-

DocTR (Document Text Recognition) es una biblioteca desarrollada por Mindee (2021) orientada a la investigación:

-
  • Arquitectura:
- Detectores: DB, LinkNet -

- Reconocedores: CRNN, SAR, ViTSTR

-
  • Fortalezas: API limpia, orientación académica, salida estructurada de alto nivel
  • Limitaciones: Menor rendimiento en español comparado con PaddleOCR
-

Comparativa de Arquitecturas

-Tabla 2. Comparativa de soluciones OCR de código abierto. -
ModeloTipoComponentesFortalezas Clave
EasyOCREnd-to-end (det + rec)CRAFT + CRNN/TransformerLigero, fácil de usar, multilingüe
PaddleOCREnd-to-end (det + rec + cls)DB + SVTR/CRNNSoporte multilingüe robusto, configurable
DocTREnd-to-end (det + rec)DB/LinkNet + CRNN/SAR/ViTSTROrientado a investigación, API limpia
-Fuente: Documentación oficial de cada herramienta (JaidedAI, 2020; PaddlePaddle, 2024; Mindee, 2021). -

Optimización de Hiperparámetros

-

Fundamentos

-

La optimización de hiperparámetros (HPO) busca encontrar la configuración de parámetros que maximiza (o minimiza) una métrica objetivo (Feurer & Hutter, 2019). A diferencia de los parámetros del modelo (pesos), los hiperparámetros no se aprenden durante el entrenamiento.

-

Los métodos de HPO incluyen:

-
  • Grid Search: Búsqueda exhaustiva en una rejilla predefinida
  • Random Search: Muestreo aleatorio del espacio de búsqueda (Bergstra & Bengio, 2012)
  • Bayesian Optimization: Modelado probabilístico de la función objetivo (Bergstra et al., 2011)
  • Algoritmos evolutivos: Optimización inspirada en evolución biológica
-

Ray Tune y Optuna

-Ray Tune es un framework de optimización de hiperparámetros escalable (Liaw et al., 2018) que permite: -
  • Ejecución paralela de experimentos
  • Early stopping de configuraciones poco prometedoras
  • Integración con múltiples algoritmos de búsqueda
-Optuna es una biblioteca de optimización bayesiana (Akiba et al., 2019) que implementa: -
  • Tree-structured Parzen Estimator (TPE)
  • Pruning de trials no prometedores
  • Visualización de resultados
-

La combinación Ray Tune + Optuna permite búsquedas eficientes en espacios de alta dimensionalidad.

-`mermaid -

flowchart TD

-

subgraph "Ray Tune"

-

A["Espacio de
búsqueda"]

-

B["Scheduler
(gestión de trials)"]

-

C["Trial 1"]

-

D["Trial 2"]

-

E["Trial N"]

-

end

-

subgraph "Optuna (TPE)"

-

F["Modelo probabilístico
de la función objetivo"]

-

G["Sugiere nueva
configuración"]

-

end

-

subgraph "Evaluación"

-

H["Ejecuta modelo OCR
con config"]

-

I["Calcula métricas
(CER, WER)"]

-

end

-

A --> B

-

B --> C & D & E

-

C & D & E --> H

-

H --> I

-

I -->|"Resultados"| F

-

F --> G

-

G -->|"Nueva config"| B

-

style A fill:#fff3e0

-

style I fill:#e8f5e9

-`` -Figura 2. Arquitectura de optimización de hiperparámetros con Ray Tune y Optuna. -

HPO en Sistemas OCR

-

La aplicación de HPO a sistemas OCR ha sido explorada principalmente en el contexto de:

-
  1. Preprocesamiento de imagen: Optimización de parámetros de binarización, filtrado y escalado (Liang et al., 2005)
-
  1. Arquitecturas de detección: Ajuste de umbrales de confianza y NMS (Non-Maximum Suppression)
-
  1. Post-procesamiento: Optimización de corrección ortográfica y modelos de lenguaje
-

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.

-

Datasets y Benchmarks para Español

-

Los principales recursos para evaluación de OCR en español incluyen:

-
  • FUNSD-ES: Versión en español del dataset de formularios
  • MLT (ICDAR): Multi-Language Text dataset con muestras en español
  • Documentos académicos: Utilizados en este trabajo (instrucciones TFE de UNIR)
-

Los trabajos previos en OCR para español se han centrado principalmente en:

-
  1. Digitalización de archivos históricos (manuscritos coloniales)
  2. Procesamiento de documentos de identidad
  3. Reconocimiento de texto en escenas naturales
-

La optimización de hiperparámetros para documentos académicos en español representa una contribución original de este trabajo.

-

2.3. Conclusiones

-

Este capítulo ha presentado:

-
  1. Los fundamentos del OCR moderno y su pipeline de detección-reconocimiento
  2. Las tres principales soluciones de código abierto: EasyOCR, PaddleOCR y DocTR
  3. Los métodos de optimización de hiperparámetros, con énfasis en Ray Tune y Optuna
  4. Las particularidades del OCR para el idioma español
-

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.

-
-

3. Objetivos concretos y metodología de trabajo

-

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.

-

3.1. Objetivo general

-
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.
-

Justificación SMART del Objetivo General

-
CriterioCumplimiento
Específico (S)Se define claramente qué se quiere lograr: optimizar PaddleOCR mediante ajuste de hiperparámetros para documentos en español
Medible (M)Se establece una métrica cuantificable: CER < 2%
Alcanzable (A)Es viable dado que: (1) PaddleOCR permite configuración de hiperparámetros, (2) Ray Tune posibilita búsqueda automatizada, (3) No se requiere GPU
Relevante (R)El impacto es demostrable: mejora la extracción de texto en documentos académicos sin costes adicionales de infraestructura
Temporal (T)El plazo es un cuatrimestre, correspondiente al TFM
-

3.2. Objetivos específicos

-

OE1: Comparar soluciones OCR de código abierto

-
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.
-

OE2: Preparar un dataset de evaluación

-
Construir un dataset estructurado de imágenes de documentos académicos en español con su texto de referencia (ground truth) extraído del PDF original.
-

OE3: Identificar hiperparámetros críticos

-
Analizar la correlación entre los hiperparámetros de PaddleOCR y las métricas de error para identificar los parámetros con mayor impacto en el rendimiento.
-

OE4: Optimizar hiperparámetros con Ray Tune

-
Ejecutar una búsqueda automatizada de hiperparámetros utilizando Ray Tune con Optuna, evaluando al menos 50 configuraciones diferentes.
-

OE5: Validar la configuración optimizada

-
Comparar el rendimiento de la configuración baseline versus la configuración optimizada sobre el dataset completo, documentando la mejora obtenida.
-

3.3. Metodología del trabajo

-

3.3.1. Visión General

-

``mermaid

-

flowchart TD

-

A["Fase 1: Preparación del Dataset

-

• Conversión PDF → Imágenes (300 DPI)

-

• Extracción de texto de referencia (PyMuPDF)

-

• Estructura: carpetas img/ y txt/ pareadas"]

-

B["Fase 2: Benchmark Comparativo

-

• Evaluación de EasyOCR, PaddleOCR, DocTR

-

• Métricas: CER, WER

-

• Selección del modelo base"]

-

C["Fase 3: Definición del Espacio de Búsqueda

-

• Identificación de hiperparámetros configurables

-

• Definición de rangos y distribuciones

-

• Configuración de Ray Tune + Optuna"]

-

D["Fase 4: Optimización de Hiperparámetros

-

• Ejecución de 64 trials con Ray Tune

-

• Paralelización (2 trials concurrentes)

-

• Registro de métricas y configuraciones"]

-

E["Fase 5: Validación y Análisis

-

• Comparación baseline vs optimizado

-

• Análisis de correlaciones

-

• Documentación de resultados"]

-

A --> B --> C --> D --> E

-` -

3.3.2. Fase 1: Preparación del Dataset

-

Fuente de Datos

-

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.

-

Proceso de Conversión

-

El script prepare_dataset.ipynb implementa:

-
  1. Conversión PDF a imágenes:
- Biblioteca: PyMuPDF (fitz) -

- Resolución: 300 DPI

-

- Formato de salida: PNG

-
  1. Extracción de texto de referencia:
- Método: page.get_text("dict") de PyMuPDF -

- Preservación de estructura de líneas

-

- Tratamiento de texto vertical/marginal

-

- Normalización de espacios y saltos de línea

-

Estructura del Dataset

-`mermaid -

flowchart LR

-

dataset["dataset/"] --> d0["0/"]

-

d0 --> pdf["instrucciones.pdf"]

-

d0 --> img["img/"]

-

img --> img1["page_0001.png"]

-

img --> img2["page_0002.png"]

-

img --> imgN["..."]

-

d0 --> txt["txt/"]

-

txt --> txt1["page_0001.txt"]

-

txt --> txt2["page_0002.txt"]

-

txt --> txtN["..."]

-

dataset --> dots["..."]

-` -

Clase ImageTextDataset

-

Se implementó una clase Python para cargar pares imagen-texto:

-`python -

class ImageTextDataset:

-

def __init__(self, root):

-

# Carga pares (imagen, texto) de carpetas pareadas

-

def __getitem__(self, idx):

-

# Retorna (PIL.Image, str)

-` -

3.3.3. Fase 2: Benchmark Comparativo

-

Modelos Evaluados

-
ModeloVersiónConfiguración
EasyOCR-Idiomas: ['es', 'en']
PaddleOCRPP-OCRv5Modelos server_det + server_rec
DocTR-db_resnet50 + sar_resnet31
-

Métricas de Evaluación

-

Se utilizó la biblioteca jiwer para calcular:

-`python -

from jiwer import wer, cer

-

def evaluate_text(reference, prediction):

-

return {

-

'WER': wer(reference, prediction),

-

'CER': cer(reference, prediction)

-

}

-` -

3.3.4. Fase 3: Espacio de Búsqueda

-

Hiperparámetros Seleccionados

-
ParámetroTipoRango/ValoresDescripción
use_doc_orientation_classifyBooleano[True, False]Clasificación de orientación del documento
use_doc_unwarpingBooleano[True, False]Corrección de deformación del documento
textline_orientationBooleano[True, False]Clasificación de orientación de línea de texto
text_det_threshContinuo[0.0, 0.7]Umbral de detección de píxeles de texto
text_det_box_threshContinuo[0.0, 0.7]Umbral de caja de detección
text_det_unclip_ratioFijo0.0Coeficiente de expansión (fijado)
text_rec_score_threshContinuo[0.0, 0.7]Umbral de confianza de reconocimiento
-

Configuración de Ray Tune

-`python -

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

-

)

-

)

-` -

3.3.5. Fase 4: Ejecución de Optimización

-

Arquitectura de Ejecución

-

Debido a incompatibilidades entre Ray y PaddleOCR en el mismo proceso, se implementó una arquitectura basada en subprocesos:

-`mermaid -

flowchart LR

-

A["Ray Tune (proceso principal)"]

-

A --> B["Subprocess 1: paddle_ocr_tuning.py --config"]

-

B --> B_out["Retorna JSON con métricas"]

-

A --> C["Subprocess 2: paddle_ocr_tuning.py --config"]

-

C --> C_out["Retorna JSON con métricas"]

-` -

Script de Evaluación (paddle_ocr_tuning.py)

-

El script recibe hiperparámetros por línea de comandos:

-`bash -

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

-` -

Y retorna métricas en formato JSON:

-`json -{ -

"CER": 0.0125,

-

"WER": 0.1040,

-

"TIME": 331.09,

-

"PAGES": 5,

-

"TIME_PER_PAGE": 66.12

-

}

-` -

3.3.6. Fase 5: Validación

-

Protocolo de Validación

-
  1. Baseline: Ejecución con configuración por defecto de PaddleOCR
  2. Optimizado: Ejecución con mejor configuración encontrada
  3. Comparación: Evaluación sobre las 24 páginas del dataset completo
  4. Métricas reportadas: CER, WER, tiempo de procesamiento
-

3.3.7. Entorno de Ejecución

-

Hardware

-
ComponenteEspecificación
CPUIntel Core (especificar modelo)
RAM16 GB
GPUNo disponible (ejecución en CPU)
AlmacenamientoSSD
-

Software

-
ComponenteVersión
Sistema OperativoWindows 10/11
Python3.11.9
PaddleOCR3.3.2
PaddlePaddle3.2.2
Ray2.52.1
Optuna4.6.0
-

3.3.8. Limitaciones Metodológicas

-
  1. Tamaño del dataset: El dataset contiene 24 páginas de un único tipo de documento. Resultados pueden no generalizar a otros formatos.
-
  1. Ejecución en CPU: Los tiempos de procesamiento (~70s/página) serían significativamente menores con GPU.
-
  1. Ground truth imperfecto: El texto de referencia extraído de PDF puede contener errores en documentos con layouts complejos.
-
  1. Parámetro fijo: text_det_unclip_ratio` quedó fijado en 0.0 durante todo el experimento por decisión de diseño inicial.
-

3.4. Resumen del capítulo

-

Este capítulo ha establecido:

-
  1. Un objetivo general SMART: alcanzar CER < 2% mediante optimización de hiperparámetros
  2. Cinco objetivos específicos medibles y alcanzables
  3. Una metodología experimental en cinco fases claramente definidas
  4. El espacio de búsqueda de hiperparámetros y la configuración de Ray Tune
  5. Las limitaciones reconocidas del enfoque
-

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.

-
-

4. Desarrollo específico de la contribución

-

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.

-

4.1. Planteamiento de la comparativa

-

4.1.1. Introducción

-

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 ocr_benchmark_notebook.ipynb del repositorio. El objetivo es identificar el modelo base más prometedor para la posterior fase de optimización de hiperparámetros.

-

4.1.2. Configuración del Experimento

-

Dataset de Evaluación

-

Se utilizó el documento "Instrucciones para la redacción y elaboración del TFE" del Máster Universitario en Inteligencia Artificial de UNIR, ubicado en la carpeta instructions/.

-Tabla 3. Características del dataset de evaluación. -
CaracterísticaValor
Número de páginas evaluadas5 (páginas 1-5 en benchmark inicial)
FormatoPDF digital (no escaneado)
IdiomaEspañol
Resolución de conversión300 DPI
-Fuente: Elaboración propia. -

Configuración de los Modelos

-

Según el código en ocr_benchmark_notebook.ipynb:

-EasyOCR: -

``python

-

easyocr_reader = easyocr.Reader(['es', 'en']) # Spanish and English

-` -PaddleOCR (PP-OCRv5): -`python -

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,

-

)

-` -

Versión utilizada: PaddleOCR 3.2.0 (según output del notebook)

-DocTR: -`python -

doctr_model = ocr_predictor(det_arch="db_resnet50", reco_arch="sar_resnet31", pretrained=True)

-` -

Métricas de Evaluación

-

Se utilizó la biblioteca jiwer para calcular CER y WER:

-`python -

from jiwer import wer, cer

-

def evaluate_text(reference, prediction):

-

return {'WER': wer(reference, prediction), 'CER': cer(reference, prediction)}

-` -

4.1.3. Resultados del Benchmark

-

Resultados de PaddleOCR (Datos del CSV)

-

Del archivo results/ai_ocr_benchmark_finetune_results_20251206_113206.csv, se obtienen los siguientes resultados de PaddleOCR para las páginas 5-9 del documento:

-Tabla 4. Resultados de PaddleOCR por página (benchmark inicial). -
PáginaWERCER
512.16%6.33%
612.81%6.40%
711.06%6.24%
88.13%1.54%
910.61%5.58%
-Fuente: results/ai_ocr_benchmark_finetune_results_20251206_113206.csv. -Promedio PaddleOCR (páginas 5-9): -
  • CER medio: ~5.22%
  • WER medio: ~10.95%
-

Comparativa de Modelos

-

Según la documentación del notebook ocr_benchmark_notebook.ipynb, los tres modelos evaluados representan diferentes paradigmas de OCR:

-Tabla 5. Comparativa de arquitecturas OCR evaluadas. -
ModeloTipoComponentesFortalezas Clave
EasyOCREnd-to-end (det + rec)DB + CRNN/TransformerLigero, fácil de usar, multilingüe
PaddleOCR (PP-OCR)End-to-end (det + rec + cls)DB + SRN/CRNNSoporte multilingüe robusto, pipeline configurable
DocTREnd-to-end (det + rec)DB/LinkNet + CRNN/SAR/VitSTROrientado a investigación, API limpia
-Fuente: Documentación oficial de cada herramienta (JaidedAI, 2020; PaddlePaddle, 2024; Mindee, 2021). -

Ejemplo de Salida OCR

-

Del archivo CSV, un ejemplo de predicción de PaddleOCR para la página 8:

-
"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."
-Errores observados en este ejemplo: -
  • titulacióon en lugar de titulación (carácter duplicado)
  • Apa en lugar de APA (capitalización)
-

4.1.4. Justificación de la Selección de PaddleOCR

-

Criterios de Selección

-

Basándose en los resultados obtenidos y la documentación del benchmark:

-
  1. Rendimiento: PaddleOCR obtuvo CER entre 1.54% y 6.40% en las páginas evaluadas
  2. Configurabilidad: PaddleOCR ofrece múltiples hiperparámetros ajustables:
- Umbrales de detección (text_det_thresh, text_det_box_thresh) -

- Umbral de reconocimiento (text_rec_score_thresh)

-

- Componentes opcionales (use_textline_orientation, use_doc_orientation_classify, use_doc_unwarping)

-
  1. Documentación oficial: [PaddleOCR Documentation](https://www.paddleocr.ai/v3.0.0/en/version3.x/pipeline_usage/OCR.html)
-

Decisión

-Se selecciona PaddleOCR (PP-OCRv5) para la fase de optimización debido a: -
  • Resultados iniciales prometedores (CER ~5%)
  • Alta configurabilidad de hiperparámetros de inferencia
  • Pipeline modular que permite experimentación
-

4.1.5. Limitaciones del Benchmark

-
  1. Tamaño reducido: Solo 5 páginas evaluadas en el benchmark comparativo inicial
  2. Único tipo de documento: Documentos académicos de UNIR únicamente
  3. Ground truth: El texto de referencia se extrajo automáticamente del PDF, lo cual puede introducir errores en layouts complejos
-

4.1.6. Resumen de la Sección

-

Esta sección ha presentado:

-
  1. La configuración del benchmark según ocr_benchmark_notebook.ipynb
  2. Los resultados cuantitativos de PaddleOCR del archivo CSV de resultados
  3. La justificación de la selección de PaddleOCR para optimización
-Fuentes de datos utilizadas: -
  • ocr_benchmark_notebook.ipynb: Código del benchmark
  • results/ai_ocr_benchmark_finetune_results_20251206_113206.csv: Resultados numéricos
  • Documentación oficial de PaddleOCR
-

4.2. Desarrollo de la comparativa: Optimización de hiperparámetros

-

4.2.1. Introducción

-

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 src/paddle_ocr_fine_tune_unir_raytune.ipynb y los resultados se almacenaron en src/raytune_paddle_subproc_results_20251207_192320.csv.

-

4.2.2. Configuración del Experimento

-

Entorno de Ejecución

-

Según los outputs del notebook:

-Tabla 6. Entorno de ejecución del experimento. -
ComponenteVersión/Especificación
Python3.11.9
PaddlePaddle3.2.2
PaddleOCR3.3.2
Ray2.52.1
GPUNo disponible (CPU only)
-Fuente: Outputs del notebook src/paddle_ocr_fine_tune_unir_raytune.ipynb. -

Dataset

-

Se utilizó un dataset estructurado en src/dataset/ creado mediante el notebook src/prepare_dataset.ipynb:

-
  • Estructura: Carpetas con subcarpetas img/ y txt/ pareadas
  • Páginas evaluadas por trial: 5 (páginas 5-10 del documento)
  • Gestión de datos: Clase ImageTextDataset en src/dataset_manager.py
-

Espacio de Búsqueda

-

Según el código del notebook, se definió el siguiente espacio de búsqueda:

-`python -

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),

-

}

-` -Descripción de parámetros (según documentación de PaddleOCR): -
ParámetroDescripción
use_doc_orientation_classifyClasificación de orientación del documento
use_doc_unwarpingCorrección de deformación del documento
textline_orientationClasificación de orientación de línea de texto
text_det_threshUmbral de detección de píxeles de texto
text_det_box_threshUmbral de caja de detección
text_det_unclip_ratioCoeficiente de expansión (fijado en 0.0)
text_rec_score_threshUmbral de confianza de reconocimiento
-

Configuración de Ray Tune

-`python -

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

-

)

-` -
  • Métrica objetivo: CER (minimizar)
  • Algoritmo de búsqueda: Optuna (TPE - Tree-structured Parzen Estimator)
  • Número de trials: 64
  • Trials concurrentes: 2
-

4.2.3. Resultados de la Optimización

-

Estadísticas Descriptivas

-

Del archivo CSV de resultados (raytune_paddle_subproc_results_20251207_192320.csv):

-Tabla 7. Estadísticas descriptivas de los 64 trials de Ray Tune. -
EstadísticaCERWERTiempo (s)Tiempo/Página (s)
count64646464
mean5.25%14.28%347.6169.42
std11.03%10.75%7.881.57
min1.15%9.89%320.9764.10
25%1.20%10.04%344.2468.76
50%1.23%10.20%346.4269.19
75%4.03%13.20%350.1469.93
max51.61%59.45%368.5773.63
-Fuente: src/raytune_paddle_subproc_results_20251207_192320.csv. -

Mejor Configuración Encontrada

-

Según el análisis del notebook:

-` -

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

-` -

Análisis de Correlación

-

Correlación de Pearson entre parámetros y métricas de error (del notebook):

-Correlación con CER: -
ParámetroCorrelación
CER1.000
config/text_det_box_thresh0.226
config/text_rec_score_thresh-0.161
config/text_det_thresh-0.523
config/text_det_unclip_ratioNaN
-Correlación con WER: -
ParámetroCorrelación
WER1.000
config/text_det_box_thresh0.227
config/text_rec_score_thresh-0.173
config/text_det_thresh-0.521
config/text_det_unclip_ratioNaN
-Hallazgo clave: El parámetro text_det_thresh muestra la correlación más fuerte (-0.52), indicando que valores más altos de este umbral tienden a reducir el error. -

Impacto del Parámetro textline_orientation

-

Según el análisis del notebook, este parámetro booleano tiene el mayor impacto:

-Tabla 8. Impacto del parámetro textline_orientation en las métricas de error. -
textline_orientationCER MedioWER Medio
True~3.76%~12.73%
False~12.40%~21.71%
-Fuente: Análisis del notebook src/paddle_ocr_fine_tune_unir_raytune.ipynb. -Interpretación: -

El CER medio es ~3.3x menor con textline_orientation=True (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.

-`mermaid -

%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#0098CD'}}}%%

-

xychart-beta

-

title "Impacto de textline_orientation en CER"

-

x-axis ["textline_orientation=False", "textline_orientation=True"]

-

y-axis "CER (%)" 0 --> 15

-

bar [12.40, 3.76]

-` -Figura 3. Comparación del CER medio según el valor del parámetro textline_orientation. -

Análisis de Fallos

-

Los trials con CER muy alto (>40%) se produjeron cuando:

-
  • text_det_thresh < 0.1 (valores muy bajos)
  • textline_orientation = False
-

Ejemplo de trial con fallo catastrófico:

-
  • CER: 51.61%
  • WER: 59.45%
  • Configuración: text_det_thresh=0.017, textline_orientation=True
-

4.2.4. Comparación Baseline vs Optimizado

-

Resultados sobre Dataset Completo (24 páginas)

-

Del análisis final del notebook ejecutando sobre las 24 páginas:

-Tabla 9. Comparación baseline vs configuración optimizada (24 páginas). -
ModeloCERWER
PaddleOCR (Baseline)7.78%14.94%
PaddleOCR-HyperAdjust1.49%7.62%
-Fuente: Ejecución final en notebook src/paddle_ocr_fine_tune_unir_raytune.ipynb. -

Métricas de Mejora

-Tabla 10. Análisis de la mejora obtenida. -
MétricaBaselineOptimizadoMejora AbsolutaReducción Error
CER7.78%1.49%-6.29 pp80.9%
WER14.94%7.62%-7.32 pp49.0%
-Fuente: Elaboración propia a partir de los resultados experimentales. -

Interpretación (del notebook)

-
"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%."
-`mermaid -

%%{init: {'theme': 'base'}}%%

-

xychart-beta

-

title "Comparación Baseline vs Optimizado (24 páginas)"

-

x-axis ["CER", "WER"]

-

y-axis "Tasa de error (%)" 0 --> 16

-

bar "Baseline" [7.78, 14.94]

-

bar "Optimizado" [1.49, 7.62]

-` -Figura 4. Comparación de métricas de error entre configuración baseline y optimizada. -Impacto práctico: En un documento de 10,000 caracteres: -
  • Baseline: ~778 caracteres con error
  • Optimizado: ~149 caracteres con error
  • Diferencia: ~629 caracteres menos con errores
-

4.2.5. Tiempo de Ejecución

-
MétricaValor
Tiempo total del experimento~6 horas (64 trials × ~6 min/trial)
Tiempo medio por trial367.72 segundos
Tiempo medio por página69.42 segundos
Total páginas procesadas64 trials × 5 páginas = 320 evaluaciones
-

4.2.6. Resumen de la Sección

-

Esta sección ha presentado:

-
  1. Configuración del experimento: 64 trials con Ray Tune + Optuna sobre 7 hiperparámetros
  2. Resultados estadísticos: CER medio 5.25%, CER mínimo 1.15%
  3. Hallazgos clave:
- textline_orientation=True es crítico (reduce CER ~70%) -

- text_det_thresh tiene correlación -0.52 con CER

-

- Valores bajos de text_det_thresh (<0.1) causan fallos catastróficos

-
  1. Mejora final: CER reducido de 7.78% a 1.49% (reducción del 80.9%)
-Fuentes de datos: -
  • src/paddle_ocr_fine_tune_unir_raytune.ipynb: Código del experimento
  • src/raytune_paddle_subproc_results_20251207_192320.csv: Resultados de 64 trials
  • src/paddle_ocr_tuning.py: Script de evaluación
-

4.3. Discusión y análisis de resultados

-

4.3.1. Introducción

-

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.

-

4.3.2. Resumen de Resultados

-

Resultados del Benchmark Comparativo

-

Del archivo results/ai_ocr_benchmark_finetune_results_20251206_113206.csv, PaddleOCR con configuración inicial (use_textline_orientation=True) obtuvo los siguientes resultados en las páginas 5-9:

-
PáginaWERCER
512.16%6.33%
612.81%6.40%
711.06%6.24%
88.13%1.54%
910.61%5.58%
Promedio10.95%5.22%
-

Resultados de la Optimización con Ray Tune

-

Del archivo src/raytune_paddle_subproc_results_20251207_192320.csv (64 trials):

-
MétricaValor
CER mínimo1.15%
CER medio5.25%
CER máximo51.61%
WER mínimo9.89%
WER medio14.28%
WER máximo59.45%
-

Comparación Final (Dataset Completo - 24 páginas)

-

Resultados del notebook src/paddle_ocr_fine_tune_unir_raytune.ipynb:

-
ModeloCERPrecisión CaracteresWERPrecisión Palabras
PaddleOCR (Baseline)7.78%92.22%14.94%85.06%
PaddleOCR-HyperAdjust1.49%98.51%7.62%92.38%
-

4.3.3. Análisis de Resultados

-

Mejora Obtenida

-
Forma de MediciónValor
Mejora en precisión de caracteres (absoluta)+6.29 puntos porcentuales
Reducción del CER (relativa)80.9%
Mejora en precisión de palabras (absoluta)+7.32 puntos porcentuales
Reducción del WER (relativa)49.0%
Precisión final de caracteres98.51%
-

Impacto de Hiperparámetros Individuales

-Parámetro textline_orientation -

Este parámetro booleano demostró ser el más influyente:

-
ValorCER MedioImpacto
True~3.76%Rendimiento óptimo
False~12.40%3.3x peor
-Reducción del CER: 69.7% cuando se habilita la clasificación de orientación de línea. -Parámetro text_det_thresh -

Correlación con CER: -0.523 (la más fuerte de los parámetros continuos)

-
RangoComportamiento
< 0.1Fallos catastróficos (CER 40-50%)
0.3 - 0.6Rendimiento óptimo
Valor óptimo0.4690
-Parámetros con menor impacto -
ParámetroCorrelación con CERValor óptimo
text_det_box_thresh+0.2260.5412
text_rec_score_thresh-0.1610.6350
use_doc_orientation_classify-False
use_doc_unwarping-False
-

Configuración Óptima Final

-`python -

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,

-

}

-` -

4.3.4. Discusión

-

Hallazgos Principales

-
  1. Importancia de la clasificación de orientación de línea: El parámetro textline_orientation=True 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.
-
  1. Umbral de detección crítico: El parámetro text_det_thresh presenta un umbral mínimo efectivo (~0.1). Valores inferiores generan demasiados falsos positivos en la detección, corrompiendo el reconocimiento posterior.
-
  1. Componentes opcionales innecesarios: Para documentos académicos digitales (no escaneados), los módulos de corrección de orientación de documento (use_doc_orientation_classify) y corrección de deformación (use_doc_unwarping) no aportan mejora e incluso pueden introducir overhead.
-

Interpretación de la Correlación Negativa

-

La correlación negativa de text_det_thresh (-0.52) con el CER indica que:

-
  • Umbrales más altos filtran detecciones de baja confianza
  • Esto reduce falsos positivos que generan texto erróneo
  • El reconocimiento es más preciso con menos regiones pero más confiables
-

Limitaciones de los Resultados

-
  1. Generalización: Los resultados se obtuvieron sobre documentos de un único tipo (instrucciones académicas UNIR). La configuración óptima puede variar para otros tipos de documentos.
-
  1. Ground truth automático: El texto de referencia se extrajo programáticamente del PDF. En layouts complejos, esto puede introducir errores en la evaluación.
-
  1. Ejecución en CPU: Los tiempos reportados (~69s/página) corresponden a ejecución en CPU. Con GPU, los tiempos serían significativamente menores.
-
  1. Parámetro fijo: text_det_unclip_ratio permaneció fijo en 0.0 durante todo el experimento por decisión de diseño.
-

Comparación con Objetivos

-
ObjetivoMetaResultadoCumplimiento
OE1: Comparar soluciones OCREvaluar EasyOCR, PaddleOCR, DocTRPaddleOCR seleccionado✓
OE2: Preparar datasetConstruir dataset estructuradoDataset de 24 páginas✓
OE3: Identificar hiperparámetros críticosAnalizar correlacionestextline_orientation y text_det_thresh identificados✓
OE4: Optimizar con Ray TuneMínimo 50 configuraciones64 trials ejecutados✓
OE5: Validar configuraciónDocumentar mejoraCER 7.78% → 1.49%✓
Objetivo GeneralCER < 2%CER = 1.49%✓
-

4.3.5. Implicaciones Prácticas

-

Recomendaciones de Configuración

-

Para documentos académicos en español similares a los evaluados:

-
  1. Obligatorio: use_textline_orientation=True
  2. Recomendado: text_det_thresh entre 0.4 y 0.5
  3. Opcional: text_det_box_thresh ~0.5, text_rec_score_thresh >0.6
  4. No recomendado: Habilitar use_doc_orientation_classify o use_doc_unwarping para documentos digitales
-

Impacto Cuantitativo

-

En un documento típico de 10,000 caracteres:

-
ConfiguraciónErrores estimados
Baseline~778 caracteres
Optimizada~149 caracteres
Reducción629 caracteres menos con errores
-

Aplicabilidad

-

Esta metodología de optimización es aplicable cuando:

-
  • No se dispone de recursos GPU para fine-tuning
  • El modelo preentrenado ya tiene soporte para el idioma objetivo
  • Se busca mejorar rendimiento sin reentrenar
-

4.3.6. Resumen de la Sección

-

Esta sección ha presentado:

-
  1. Los resultados consolidados del benchmark y la optimización
  2. El análisis del impacto de cada hiperparámetro
  3. La configuración óptima identificada
  4. La discusión de limitaciones y aplicabilidad
  5. El cumplimiento de los objetivos planteados
-Resultado principal: 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 < 2%. -Fuentes de datos: -
  • results/ai_ocr_benchmark_finetune_results_20251206_113206.csv
  • src/raytune_paddle_subproc_results_20251207_192320.csv
  • src/paddle_ocr_fine_tune_unir_raytune.ipynb`
-
-

5. Conclusiones y trabajo futuro

-

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.

-

5.1. Conclusiones

-

5.1.1. Conclusiones Generales

-

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.

-

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:

-
MétricaObjetivoResultado
CER< 2%1.49%
-

5.1.2. Conclusiones Específicas

-Respecto a OE1 (Comparativa de soluciones OCR): -
  • Se evaluaron tres soluciones OCR de código abierto: EasyOCR, PaddleOCR (PP-OCRv5) y DocTR
  • PaddleOCR demostró el mejor rendimiento base para documentos en español
  • La configurabilidad del pipeline de PaddleOCR lo hace idóneo para optimización
-Respecto a OE2 (Preparación del dataset): -
  • Se construyó un dataset estructurado con 24 páginas de documentos académicos
  • La clase ImageTextDataset facilita la carga de pares imagen-texto
  • El ground truth se extrajo automáticamente del PDF mediante PyMuPDF
-Respecto a OE3 (Identificación de hiperparámetros críticos): -
  • El parámetro textline_orientation es el más influyente: reduce el CER en un 69.7% cuando está habilitado
  • El umbral text_det_thresh presenta la correlación más fuerte (-0.52) con el CER
  • Los parámetros de corrección de documento (use_doc_orientation_classify, use_doc_unwarping) no aportan mejora en documentos digitales
-Respecto a OE4 (Optimización con Ray Tune): -
  • Se ejecutaron 64 trials con el algoritmo OptunaSearch
  • El tiempo total del experimento fue aproximadamente 6 horas (en CPU)
  • La arquitectura basada en subprocesos permitió superar incompatibilidades entre Ray y PaddleOCR
-Respecto a OE5 (Validación de la configuración): -
  • Se validó la configuración óptima sobre el dataset completo de 24 páginas
  • La mejora obtenida fue del 80.9% en reducción del CER (7.78% → 1.49%)
  • La precisión de caracteres alcanzó el 98.51%
-

5.1.3. Hallazgos Clave

-
  1. Arquitectura sobre umbrales: Un único parámetro booleano (textline_orientation) tiene más impacto que todos los umbrales continuos combinados.
-
  1. Umbrales mínimos efectivos: Valores de text_det_thresh < 0.1 causan fallos catastróficos (CER >40%).
-
  1. Simplicidad para documentos digitales: Para documentos PDF digitales (no escaneados), los módulos de corrección de orientación y deformación son innecesarios.
-
  1. Optimización sin fine-tuning: Se puede mejorar significativamente el rendimiento de modelos preentrenados mediante ajuste de hiperparámetros de inferencia.
-

5.1.4. Contribuciones del Trabajo

-
  1. Metodología reproducible: Se documenta un proceso completo de optimización de hiperparámetros OCR con Ray Tune + Optuna.
-
  1. Análisis de hiperparámetros de PaddleOCR: Se cuantifica el impacto de cada parámetro configurable mediante correlaciones y análisis comparativo.
-
  1. Configuración óptima para español: Se proporciona una configuración validada para documentos académicos en español.
-
  1. Código fuente: Todo el código está disponible en el repositorio GitHub para reproducción y extensión.
-

5.1.5. Limitaciones del Trabajo

-
  1. Tipo de documento único: Los experimentos se realizaron únicamente sobre documentos académicos de UNIR. La generalización a otros tipos de documentos requiere validación adicional.
-
  1. Tamaño del dataset: 24 páginas es un corpus limitado para conclusiones estadísticamente robustas.
-
  1. Ground truth automático: La extracción automática del texto de referencia puede introducir errores en layouts complejos.
-
  1. Ejecución en CPU: Los tiempos de procesamiento (~69s/página) limitan la aplicabilidad en escenarios de alto volumen.
-
  1. Parámetro no explorado: text_det_unclip_ratio permaneció fijo en 0.0 durante todo el experimento.
-

5.2. Líneas de trabajo futuro

-

5.2.1. Extensiones Inmediatas

-
  1. Validación cruzada: Evaluar la configuración óptima en otros tipos de documentos en español (facturas, formularios, textos manuscritos).
-
  1. Exploración de text_det_unclip_ratio: Incluir este parámetro en el espacio de búsqueda.
-
  1. Dataset ampliado: Construir un corpus más amplio y diverso de documentos en español.
-
  1. Evaluación con GPU: Medir tiempos de inferencia con aceleración GPU.
-

5.2.2. Líneas de Investigación

-
  1. Transfer learning de hiperparámetros: Investigar si las configuraciones óptimas para un tipo de documento transfieren a otros dominios.
-
  1. Optimización multi-objetivo: Considerar simultáneamente CER, WER y tiempo de inferencia como objetivos.
-
  1. AutoML para OCR: Aplicar técnicas de AutoML más avanzadas (Neural Architecture Search, meta-learning).
-
  1. Comparación con fine-tuning: Cuantificar la brecha de rendimiento entre optimización de hiperparámetros y fine-tuning real.
-

5.2.3. Aplicaciones Prácticas

-
  1. Herramienta de configuración automática: Desarrollar una herramienta que determine automáticamente la configuración óptima para un nuevo tipo de documento.
-
  1. Integración en pipelines de producción: Implementar la configuración optimizada en sistemas reales de procesamiento documental.
-
  1. Benchmark público: Publicar un benchmark de OCR para documentos en español que facilite la comparación de soluciones.
-

5.2.4. Reflexión Final

-

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.

-

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.

-

El código fuente y los datos experimentales están disponibles públicamente para facilitar la reproducción y extensión de este trabajo.

-
-

Referencias bibliográficas {.unnumbered}

-

Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2623-2631. https://doi.org/10.1145/3292500.3330701

-

Baek, Y., Lee, B., Han, D., Yun, S., & Lee, H. (2019). Character region awareness for text detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9365-9374. https://doi.org/10.1109/CVPR.2019.00959

-

Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(1), 281-305. https://jmlr.org/papers/v13/bergstra12a.html

-

Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems, 24, 2546-2554. https://papers.nips.cc/paper/2011/hash/86e8f7ab32cfd12577bc2619bc635690-Abstract.html

-

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

-

Doran, G. T. (1981). There's a S.M.A.R.T. way to write management's goals and objectives. Management Review, 70(11), 35-36.

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Du, Y., Li, C., Guo, R., Yin, X., Liu, W., Zhou, J., Bai, Y., Yu, Z., Yang, Y., Dang, Q., & Wang, H. (2020). PP-OCR: A practical ultra lightweight OCR system. arXiv preprint arXiv:2009.09941. https://arxiv.org/abs/2009.09941

-

Du, Y., Li, C., Guo, R., Cui, C., Liu, W., Zhou, J., Lu, B., Yang, Y., Liu, Q., Hu, X., Yu, D., & Wang, H. (2023). PP-OCRv4: Mobile scene text detection and recognition. arXiv preprint arXiv:2310.05930. https://arxiv.org/abs/2310.05930

-

Feurer, M., & Hutter, F. (2019). Hyperparameter optimization. In F. Hutter, L. Kotthoff, & J. Vanschoren (Eds.), Automated machine learning: Methods, systems, challenges (pp. 3-33). Springer. https://doi.org/10.1007/978-3-030-05318-5_1

-

He, P., Huang, W., Qiao, Y., Loy, C. C., & Tang, X. (2016). Reading scene text in deep convolutional sequences. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1), 3501-3508. https://doi.org/10.1609/aaai.v30i1.10291

-

JaidedAI. (2020). EasyOCR: Ready-to-use OCR with 80+ supported languages. GitHub. https://github.com/JaidedAI/EasyOCR

-

Liang, J., Doermann, D., & Li, H. (2005). Camera-based analysis of text and documents: A survey. International Journal of Document Analysis and Recognition, 7(2), 84-104. https://doi.org/10.1007/s10032-004-0138-z

-

Liao, M., Wan, Z., Yao, C., Chen, K., & Bai, X. (2020). Real-time scene text detection with differentiable binarization. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11474-11481. https://doi.org/10.1609/aaai.v34i07.6812

-

Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J. E., & Stoica, I. (2018). Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118. https://arxiv.org/abs/1807.05118

-

Mindee. (2021). DocTR: Document Text Recognition. GitHub. https://github.com/mindee/doctr

-

Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., Elibol, M., Yang, Z., Paul, W., Jordan, M. I., & Stoica, I. (2018). Ray: A distributed framework for emerging AI applications. 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), 561-577. https://www.usenix.org/conference/osdi18/presentation/moritz

-

Morris, A. C., Maier, V., & Green, P. D. (2004). From WER and RIL to MER and WIL: Improved evaluation measures for connected speech recognition. Eighth International Conference on Spoken Language Processing. https://doi.org/10.21437/Interspeech.2004-668

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PaddlePaddle. (2024). PaddleOCR: Awesome multilingual OCR toolkits based on PaddlePaddle. GitHub. https://github.com/PaddlePaddle/PaddleOCR

-

Pearson, K. (1895). Notes on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London, 58, 240-242. https://doi.org/10.1098/rspl.1895.0041

-

PyMuPDF. (2024). PyMuPDF documentation. https://pymupdf.readthedocs.io/

-

Shi, B., Bai, X., & Yao, C. (2016). An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2298-2304. https://doi.org/10.1109/TPAMI.2016.2646371

-

Smith, R. (2007). An overview of the Tesseract OCR engine. Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), 2, 629-633. https://doi.org/10.1109/ICDAR.2007.4376991

-

Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., & Liang, J. (2017). EAST: An efficient and accurate scene text detector. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5551-5560. https://doi.org/10.1109/CVPR.2017.283

-

Zoph, B., & Le, Q. V. (2017). Neural architecture search with reinforcement learning. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1611.01578

-
-

Anexo A. Código fuente y datos analizados {.unnumbered}

-

A.1 Repositorio del Proyecto

-

El código fuente completo y los datos utilizados en este trabajo están disponibles en el siguiente repositorio:

-URL del repositorio: https://github.com/seryus/MastersThesis -

El repositorio incluye:

-
  • Notebooks de experimentación: Código completo de los experimentos realizados
  • Scripts de evaluación: Herramientas para evaluar modelos OCR
  • Dataset: Imágenes y textos de referencia utilizados
  • Resultados: Archivos CSV con los resultados de los 64 trials de Ray Tune
-

A.2 Estructura del Repositorio

-

``

-

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

-` -

A.3 Requisitos de Software

-

Para reproducir los experimentos se requieren las siguientes dependencias:

-
ComponenteVersión
Python3.11.9
PaddlePaddle3.2.2
PaddleOCR3.3.2
Ray2.52.1
Optuna4.6.0
jiwer(última versión)
PyMuPDF(última versión)
-

A.4 Instrucciones de Ejecución

-
  1. Clonar el repositorio
  2. Instalar dependencias: pip install -r requirements.txt
  3. Ejecutar el notebook src/paddle_ocr_fine_tune_unir_raytune.ipynb`
-

A.5 Licencia

-

El código se distribuye bajo licencia MIT.

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textline_orientation en CER", + "index": 6 + }, + { + "file": "figura_7.png", + "title": "Comparación Baseline vs Optimizado (24 páginas)", + "index": 7 + } +] \ No newline at end of file diff --git a/thesis_output/plantilla_individual.htm b/thesis_output/plantilla_individual.htm new file mode 100644 index 0000000..cdf2b41 --- /dev/null +++ b/thesis_output/plantilla_individual.htm @@ -0,0 +1,5321 @@ + + + + + + + + + + + + + + + + + + + + +

+

 

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Texto + +Descripción generada automáticamente

+

Universidad +Internacional de La Rioja

+

Escuela +Superior de Ingeniería y

+

Tecnología

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+

 

+

 

+

 

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Máster Universitario +en Inteligencia artificial

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Optimización de Hiperparámetros OCR +con Ray Tune para Documentos Académicos en Español

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+ + + + + + + + + + + + + + + + + +
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Trabajo fin de + estudio presentado por:

+		 +

Sergio Jiménez Jiménez

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+

Tipo de + trabajo:

+		 +

Desarrollo + Software

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+

Director/a:

+		 +

Javier Rodrigo + Villazón Terrazas

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+

Fecha:

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06.10.2025

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+ +

Resumen

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.

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Palabras clave: OCR, Reconocimiento Óptico de Caracteres, PaddleOCR, Optimización de Hiperparámetros, Ray Tune, Procesamiento de Documentos, Inteligencia Artificial

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+ + + + + + + + + + +

Abstract

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.

+

 

+

Keywords: OCR, Optical Character Recognition, PaddleOCR, Hyperparameter Optimization, Ray Tune, Document Processing, Artificial Intelligence

+

 

+ + + + + +
+ +

 

+ +

Índice de contenidos

+

1.    Introducción. 1

+

1.1.      Motivación. 1

+

1.2.      Planteamiento +del trabajo. 3

+

1.3.      Estructura +del trabajo. 3

+

2.    Contexto +y estado del arte. 4

+

2.1.      Contexto +del problema. 4

+

2.2.      Estado +del arte. 4

+

2.3.      Conclusiones. 5

+

3.    Objetivos +concretos y metodología de trabajo. 6

+

3.1.      Objetivo +general 6

+

3.2.      Objetivos +específicos. 7

+

3.3.      Metodología +del trabajo. 8

+

4.    Desarrollo específico de la contribución. 9

+

5.    Conclusiones +y trabajo futuro. 13

+

5.1.      Conclusiones. 13

+

5.2.      Líneas +de trabajo futuro. 13

+

Referencias bibliográficas. 14

+

Anexo A.     Código +fuente y datos analizados 15

+


+Índice de figuras

+

Figura 1. Ejemplo +de figura realizada para nuestro trabajo. 2

+


+Índice de tablas

+

Tabla 1. Ejemplo +de tabla con sus principales elementos. 2

+

 

+

 

+

 

+

 

+

 

+

 

+

 

+

 

+

 

+

 

+

 

+

 

+

 

+

 

+

 

+

 

+
+
+ +
+

1.   +Introducción

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.

+

1.1. Motivación

+

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.

+

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.

+

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.

+

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.

+

1.2. Planteamiento del trabajo

+

El problema central que aborda este trabajo puede formularse de la siguiente manera:

+

¿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?

+

Este planteamiento se descompone en las siguientes cuestiones específicas:

+

1.   Selección de modelo base: ¿Cuál de las soluciones OCR de código abierto disponibles (EasyOCR, PaddleOCR, DocTR) ofrece el mejor rendimiento base para documentos en español?

+

1.   Impacto de hiperparámetros: ¿Qué hiperparámetros del pipeline OCR tienen mayor influencia en las métricas de error (CER, WER)?

+

1.   Optimización automatizada: ¿Puede un proceso de búsqueda automatizada de hiperparámetros (mediante Ray Tune/Optuna) encontrar configuraciones que superen significativamente los valores por defecto?

+

1.   Viabilidad práctica: ¿Son los tiempos de inferencia y los recursos requeridos compatibles con un despliegue en entornos con recursos limitados?

+

La relevancia de este problema radica en su aplicabilidad inmediata. Una metodología reproducible para optimizar OCR sin fine-tuning beneficiaría a:

+

·     Investigadores que procesan grandes volúmenes de documentos académicos

+

·     Instituciones educativas que digitalizan archivos históricos

+

·     Pequeñas y medianas empresas que automatizan flujos documentales

+

·     Desarrolladores que integran OCR en aplicaciones con restricciones de recursos

+

1.3. Estructura del trabajo

+

El presente documento se organiza en los siguientes capítulos:

+

Capítulo 2 - Contexto y Estado del Arte: 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.

+

Capítulo 3 - Objetivos y Metodología: 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.

+

Capítulo 4 - Desarrollo Específico de la Contribución: 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.

+

Capítulo 5 - Conclusiones y Trabajo Futuro: Se resumen las contribuciones del trabajo, se discute el grado de cumplimiento de los objetivos y se proponen líneas de trabajo futuro.

+

Anexos: Se incluye el enlace al repositorio de código fuente y datos, así como tablas completas de resultados experimentales.

2.   +Contexto +y estado del arte

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.

+

2.1. Contexto del problema

+

Definición y Evolución Histórica del OCR

+

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:

+

·     Primera generación (1950-1970): Sistemas basados en plantillas que requerían fuentes específicas.

+

·     Segunda generación (1970-1990): Introducción de técnicas de extracción de características y clasificadores estadísticos.

+

·     Tercera generación (1990-2010): Modelos basados en Redes Neuronales Artificiales y Modelos Ocultos de Markov (HMM).

+

·     Cuarta generación (2010-presente): Arquitecturas de aprendizaje profundo que dominan el estado del arte.

+

Pipeline Moderno de OCR

+

Los sistemas OCR modernos siguen típicamente un pipeline de dos etapas:

+

Figura 1. Diagrama 1

+

Diagrama 1

+

Fuente: Elaboración propia.

+

 

+

1.   Detección de texto (Text Detection): Localización de regiones que contienen texto en la imagen. Las arquitecturas más utilizadas incluyen:

+

- EAST (Efficient and Accurate Scene Text Detector) - CRAFT (Character Region Awareness for Text Detection) - DB (Differentiable Binarization)

+

1.   Reconocimiento de texto (Text Recognition): Transcripción del contenido textual de las regiones detectadas. Las arquitecturas predominantes son:

+

- CRNN (Convolutional Recurrent Neural Network) con CTC loss - Arquitecturas encoder-decoder con atención - Transformers (ViTSTR, TrOCR)

+

Métricas de Evaluación

+

Las métricas estándar para evaluar sistemas OCR son:

+

Character Error Rate (CER): Se calcula como CER = (S + D + I) / N, donde S = sustituciones, D = eliminaciones, I = inserciones, N = caracteres de referencia.

+

Word Error Rate (WER): Se calcula de forma análoga pero a nivel de palabras en lugar de caracteres.

+

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%.

+

Particularidades del OCR para el Idioma Español

+

El español presenta características específicas que impactan el OCR:

+

·     Caracteres especiales: ñ, á, é, í, ó, ú, ü, ¿, ¡

+

·     Diacríticos: Los acentos pueden confundirse con ruido o artefactos

+

·     Longitud de palabras: Palabras generalmente más largas que en inglés

+

·     Puntuación: Signos de interrogación y exclamación invertidos

+

2.2. Estado del arte

+

Soluciones OCR de Código Abierto

+

EasyOCR

+

EasyOCR es una biblioteca de OCR desarrollada por Jaided AI (2020) que soporta más de 80 idiomas. Sus características principales incluyen:

+

·     Arquitectura: Detector CRAFT + Reconocedor CRNN/Transformer

+

·     Fortalezas: Facilidad de uso, soporte multilingüe amplio, bajo consumo de memoria

+

·     Limitaciones: Menor precisión en documentos complejos, opciones de configuración limitadas

+

·     Caso de uso ideal: Prototipado rápido y aplicaciones con restricciones de memoria

+

PaddleOCR

+

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:

+

·     Arquitectura:

+

- Detector: DB (Differentiable Binarization) con backbone ResNet (Liao et al., 2020) - Reconocedor: SVTR (Scene-Text Visual Transformer Recognition) - Clasificador de orientación opcional

+

·     Hiperparámetros configurables:

+

Tabla 1. Hiperparámetros configurables de PaddleOCR.

+

Parámetro

Descripción

Valor por defecto

text_det_thresh

Umbral de detección de píxeles

0.3

text_det_box_thresh

Umbral de caja de detección

0.6

text_det_unclip_ratio

Coeficiente de expansión

1.5

text_rec_score_thresh

Umbral de confianza de reconocimiento

0.5

use_textline_orientation

Clasificación de orientación

False

use_doc_orientation_classify

Clasificación de orientación de documento

False

use_doc_unwarping

Corrección de deformación

False

+

Fuente: Elaboración propia.

+

 

+

·     Fortalezas: Alta precisión, pipeline altamente configurable, modelos específicos para servidor

+

·     Limitaciones: Mayor complejidad de configuración, dependencia del framework PaddlePaddle

+

DocTR

+

DocTR (Document Text Recognition) es una biblioteca desarrollada por Mindee (2021) orientada a la investigación:

+

·     Arquitectura:

+

- Detectores: DB, LinkNet - Reconocedores: CRNN, SAR, ViTSTR

+

·     Fortalezas: API limpia, orientación académica, salida estructurada de alto nivel

+

·     Limitaciones: Menor rendimiento en español comparado con PaddleOCR

+

Comparativa de Arquitecturas

+

Tabla 2. Comparativa de soluciones OCR de código abierto.

+

Modelo

Tipo

Componentes

Fortalezas Clave

EasyOCR

End-to-end (det + rec)

CRAFT + CRNN/Transformer

Ligero, fácil de usar, multilingüe

PaddleOCR

End-to-end (det + rec + cls)

DB + SVTR/CRNN

Soporte multilingüe robusto, configurable

DocTR

End-to-end (det + rec)

DB/LinkNet + CRNN/SAR/ViTSTR

Orientado a investigación, API limpia

+

Fuente: Elaboración propia.

+

 

+

Optimización de Hiperparámetros

+

Fundamentos

+

La optimización de hiperparámetros (HPO) busca encontrar la configuración de parámetros que maximiza (o minimiza) una métrica objetivo (Feurer & Hutter, 2019). A diferencia de los parámetros del modelo (pesos), los hiperparámetros no se aprenden durante el entrenamiento.

+

Los métodos de HPO incluyen:

+

·     Grid Search: Búsqueda exhaustiva en una rejilla predefinida

+

·     Random Search: Muestreo aleatorio del espacio de búsqueda (Bergstra & Bengio, 2012)

+

·     Bayesian Optimization: Modelado probabilístico de la función objetivo (Bergstra et al., 2011)

+

·     Algoritmos evolutivos: Optimización inspirada en evolución biológica

+

Ray Tune y Optuna

+

Ray Tune es un framework de optimización de hiperparámetros escalable (Liaw et al., 2018) que permite:

+

·     Ejecución paralela de experimentos

+

·     Early stopping de configuraciones poco prometedoras

+

·     Integración con múltiples algoritmos de búsqueda

+

Optuna es una biblioteca de optimización bayesiana (Akiba et al., 2019) que implementa:

+

·     Tree-structured Parzen Estimator (TPE)

+

·     Pruning de trials no prometedores

+

·     Visualización de resultados

+

La combinación Ray Tune + Optuna permite búsquedas eficientes en espacios de alta dimensionalidad.

+

Figura 2. Diagrama 2

+

Diagrama 2

+

Fuente: Elaboración propia.

+

 

+

HPO en Sistemas OCR

+

La aplicación de HPO a sistemas OCR ha sido explorada principalmente en el contexto de:

+

1.   Preprocesamiento de imagen: Optimización de parámetros de binarización, filtrado y escalado (Liang et al., 2005)

+

1.   Arquitecturas de detección: Ajuste de umbrales de confianza y NMS (Non-Maximum Suppression)

+

1.   Post-procesamiento: Optimización de corrección ortográfica y modelos de lenguaje

+

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.

+

Datasets y Benchmarks para Español

+

Los principales recursos para evaluación de OCR en español incluyen:

+

·     FUNSD-ES: Versión en español del dataset de formularios

+

·     MLT (ICDAR): Multi-Language Text dataset con muestras en español

+

·     Documentos académicos: Utilizados en este trabajo (instrucciones TFE de UNIR)

+

Los trabajos previos en OCR para español se han centrado principalmente en:

+

1.   Digitalización de archivos históricos (manuscritos coloniales)

+

2.   Procesamiento de documentos de identidad

+

3.   Reconocimiento de texto en escenas naturales

+

La optimización de hiperparámetros para documentos académicos en español representa una contribución original de este trabajo.

+

2.3. Conclusiones

+

Este capítulo ha presentado:

+

1.   Los fundamentos del OCR moderno y su pipeline de detección-reconocimiento

+

2.   Las tres principales soluciones de código abierto: EasyOCR, PaddleOCR y DocTR

+

3.   Los métodos de optimización de hiperparámetros, con énfasis en Ray Tune y Optuna

+

4.   Las particularidades del OCR para el idioma español

+

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.

3.   +Objetivos +concretos y metodología de trabajo

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.

+

3.1. Objetivo general

+

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.

+

Justificación SMART del Objetivo General

+

Tabla 3. Tabla de datos.

+

Criterio

Cumplimiento

Específico (S)

Se define claramente qué se quiere lograr: optimizar PaddleOCR mediante ajuste de hiperparámetros para documentos en español

Medible (M)

Se establece una métrica cuantificable: CER < 2%

Alcanzable (A)

Es viable dado que: (1) PaddleOCR permite configuración de hiperparámetros, (2) Ray Tune posibilita búsqueda automatizada, (3) No se requiere GPU

Relevante (R)

El impacto es demostrable: mejora la extracción de texto en documentos académicos sin costes adicionales de infraestructura

Temporal (T)

El plazo es un cuatrimestre, correspondiente al TFM

+

Fuente: Elaboración propia.

+

 

+

3.2. Objetivos específicos

+

OE1: Comparar soluciones OCR de código abierto

+

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.

+

OE2: Preparar un dataset de evaluación

+

Construir un dataset estructurado de imágenes de documentos académicos en español con su texto de referencia (ground truth) extraído del PDF original.

+

OE3: Identificar hiperparámetros críticos

+

Analizar la correlación entre los hiperparámetros de PaddleOCR y las métricas de error para identificar los parámetros con mayor impacto en el rendimiento.

+

OE4: Optimizar hiperparámetros con Ray Tune

+

Ejecutar una búsqueda automatizada de hiperparámetros utilizando Ray Tune con Optuna, evaluando al menos 50 configuraciones diferentes.

+

OE5: Validar la configuración optimizada

+

Comparar el rendimiento de la configuración baseline versus la configuración optimizada sobre el dataset completo, documentando la mejora obtenida.

+

3.3. Metodología del trabajo

+

3.3.1. Visión General

+

Figura 3. Diagrama 3

+

Diagrama 3

+

Fuente: Elaboración propia.

+

 

+

3.3.2. Fase 1: Preparación del Dataset

+

Fuente de Datos

+

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.

+

Proceso de Conversión

+

El script prepare_dataset.ipynb implementa:

+

1.   Conversión PDF a imágenes:

+

- Biblioteca: PyMuPDF (fitz) - Resolución: 300 DPI - Formato de salida: PNG

+

1.   Extracción de texto de referencia:

+

- Método: page.get_text("dict") de PyMuPDF - Preservación de estructura de líneas - Tratamiento de texto vertical/marginal - Normalización de espacios y saltos de línea

+

Estructura del Dataset

+

Figura 4. Diagrama 4

+

Diagrama 4

+

Fuente: Elaboración propia.

+

 

+

Clase ImageTextDataset

+

Se implementó una clase Python para cargar pares imagen-texto:

+

class ImageTextDataset:
+    def __init__(self, root):
+        # Carga pares (imagen, texto) de carpetas pareadas
+
+    def __getitem__(self, idx):
+        # Retorna (PIL.Image, str)

+

3.3.3. Fase 2: Benchmark Comparativo

+

Modelos Evaluados

+

Tabla 4. Tabla de datos.

+

Modelo

Versión

Configuración

EasyOCR

-

Idiomas: ['es', 'en']

PaddleOCR

PP-OCRv5

Modelos server_det + server_rec

DocTR

-

db_resnet50 + sar_resnet31

+

Fuente: Elaboración propia.

+

 

+

Métricas de Evaluación

+

Se utilizó la biblioteca jiwer para calcular:

+

from jiwer import wer, cer
+
+def evaluate_text(reference, prediction):
+    return {
+        'WER': wer(reference, prediction),
+        'CER': cer(reference, prediction)
+    }

+

3.3.4. Fase 3: Espacio de Búsqueda

+

Hiperparámetros Seleccionados

+

Tabla 5. Tabla de datos.

+

Parámetro

Tipo

Rango/Valores

Descripción

use_doc_orientation_classify

Booleano

[True, False]

Clasificación de orientación del documento

use_doc_unwarping

Booleano

[True, False]

Corrección de deformación del documento

textline_orientation

Booleano

[True, False]

Clasificación de orientación de línea de texto

text_det_thresh

Continuo

[0.0, 0.7]

Umbral de detección de píxeles de texto

text_det_box_thresh

Continuo

[0.0, 0.7]

Umbral de caja de detección

text_det_unclip_ratio

Fijo

0.0

Coeficiente de expansión (fijado)

text_rec_score_thresh

Continuo

[0.0, 0.7]

Umbral de confianza de reconocimiento

+

Fuente: Elaboración propia.

+

 

+

Configuración de Ray Tune

+

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
+    )
+)

+

3.3.5. Fase 4: Ejecución de Optimización

+

Arquitectura de Ejecución

+

Debido a incompatibilidades entre Ray y PaddleOCR en el mismo proceso, se implementó una arquitectura basada en subprocesos:

+

Figura 5. Diagrama 5

+

Diagrama 5

+

Fuente: Elaboración propia.

+

 

+

Script de Evaluación (paddle_ocr_tuning.py)

+

El script recibe hiperparámetros por línea de comandos:

+

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

+

Y retorna métricas en formato JSON:

+

{
+    "CER": 0.0125,
+    "WER": 0.1040,
+    "TIME": 331.09,
+    "PAGES": 5,
+    "TIME_PER_PAGE": 66.12
+}

+

3.3.6. Fase 5: Validación

+

Protocolo de Validación

+

1.   Baseline: Ejecución con configuración por defecto de PaddleOCR

+

2.   Optimizado: Ejecución con mejor configuración encontrada

+

3.   Comparación: Evaluación sobre las 24 páginas del dataset completo

+

4.   Métricas reportadas: CER, WER, tiempo de procesamiento

+

3.3.7. Entorno de Ejecución

+

Hardware

+

Tabla 6. Tabla de datos.

+

Componente

Especificación

CPU

Intel Core (especificar modelo)

RAM

16 GB

GPU

No disponible (ejecución en CPU)

Almacenamiento

SSD

+

Fuente: Elaboración propia.

+

 

+

Software

+

Tabla 7. Tabla de datos.

+

Componente

Versión

Sistema Operativo

Windows 10/11

Python

3.11.9

PaddleOCR

3.3.2

PaddlePaddle

3.2.2

Ray

2.52.1

Optuna

4.6.0

+

Fuente: Elaboración propia.

+

 

+

3.3.8. Limitaciones Metodológicas

+

1.   Tamaño del dataset: El dataset contiene 24 páginas de un único tipo de documento. Resultados pueden no generalizar a otros formatos.

+

1.   Ejecución en CPU: Los tiempos de procesamiento (~70s/página) serían significativamente menores con GPU.

+

1.   Ground truth imperfecto: El texto de referencia extraído de PDF puede contener errores en documentos con layouts complejos.

+

1.   Parámetro fijo: text_det_unclip_ratio quedó fijado en 0.0 durante todo el experimento por decisión de diseño inicial.

+

3.4. Resumen del capítulo

+

Este capítulo ha establecido:

+

1.   Un objetivo general SMART: alcanzar CER < 2% mediante optimización de hiperparámetros

+

2.   Cinco objetivos específicos medibles y alcanzables

+

3.   Una metodología experimental en cinco fases claramente definidas

+

4.   El espacio de búsqueda de hiperparámetros y la configuración de Ray Tune

+

5.   Las limitaciones reconocidas del enfoque

+

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.

4.   Desarrollo +específico de la contribución

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.

+

4.1. Planteamiento de la comparativa

+

4.1.1. Introducción

+

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 ocr_benchmark_notebook.ipynb del repositorio. El objetivo es identificar el modelo base más prometedor para la posterior fase de optimización de hiperparámetros.

+

4.1.2. Configuración del Experimento

+

Dataset de Evaluación

+

Se utilizó el documento "Instrucciones para la redacción y elaboración del TFE" del Máster Universitario en Inteligencia Artificial de UNIR, ubicado en la carpeta instructions/.

+

Tabla 8. Tabla 3. Características del dataset de evaluación.

+

Característica

Valor

Número de páginas evaluadas

5 (páginas 1-5 en benchmark inicial)

Formato

PDF digital (no escaneado)

Idioma

Español

Resolución de conversión

300 DPI

+

Fuente: Elaboración propia.

+

 

+

Configuración de los Modelos

+

Según el código en ocr_benchmark_notebook.ipynb:

+

EasyOCR:

+

easyocr_reader = easyocr.Reader(['es', 'en'])  # Spanish and English

+

PaddleOCR (PP-OCRv5):

+

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,
+)

+

Versión utilizada: PaddleOCR 3.2.0 (según output del notebook)

+

DocTR:

+

doctr_model = ocr_predictor(det_arch="db_resnet50", reco_arch="sar_resnet31", pretrained=True)

+

Métricas de Evaluación

+

Se utilizó la biblioteca jiwer para calcular CER y WER:

+

from jiwer import wer, cer
+
+def evaluate_text(reference, prediction):
+    return {'WER': wer(reference, prediction), 'CER': cer(reference, prediction)}

+

4.1.3. Resultados del Benchmark

+

Resultados de PaddleOCR (Datos del CSV)

+

Del archivo results/ai_ocr_benchmark_finetune_results_20251206_113206.csv, se obtienen los siguientes resultados de PaddleOCR para las páginas 5-9 del documento:

+

Tabla 9. Tabla 4. Resultados de PaddleOCR por página (benchmark inicial).

+

Página

WER

CER

5

12.16%

6.33%

6

12.81%

6.40%

7

11.06%

6.24%

8

8.13%

1.54%

9

10.61%

5.58%

+

Fuente: Elaboración propia.

+

 

+

Promedio PaddleOCR (páginas 5-9):

+

·     CER medio: ~5.22%

+

·     WER medio: ~10.95%

+

Comparativa de Modelos

+

Según la documentación del notebook ocr_benchmark_notebook.ipynb, los tres modelos evaluados representan diferentes paradigmas de OCR:

+

Tabla 10. Tabla 5. Comparativa de arquitecturas OCR evaluadas.

+

Modelo

Tipo

Componentes

Fortalezas Clave

EasyOCR

End-to-end (det + rec)

DB + CRNN/Transformer

Ligero, fácil de usar, multilingüe

PaddleOCR (PP-OCR)

End-to-end (det + rec + cls)

DB + SRN/CRNN

Soporte multilingüe robusto, pipeline configurable

DocTR

End-to-end (det + rec)

DB/LinkNet + CRNN/SAR/VitSTR

Orientado a investigación, API limpia

+

Fuente: Elaboración propia.

+

 

+

Ejemplo de Salida OCR

+

Del archivo CSV, un ejemplo de predicción de PaddleOCR para la página 8:

+

"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."

+

Errores observados en este ejemplo:

+

·     titulacióon en lugar de titulación (carácter duplicado)

+

·     Apa en lugar de APA (capitalización)

+

4.1.4. Justificación de la Selección de PaddleOCR

+

Criterios de Selección

+

Basándose en los resultados obtenidos y la documentación del benchmark:

+

1.   Rendimiento: PaddleOCR obtuvo CER entre 1.54% y 6.40% en las páginas evaluadas

+

2.   Configurabilidad: PaddleOCR ofrece múltiples hiperparámetros ajustables:

+

- Umbrales de detección (text_det_thresh, text_det_box_thresh) - Umbral de reconocimiento (text_rec_score_thresh) - Componentes opcionales (use_textline_orientation, use_doc_orientation_classify, use_doc_unwarping)

+

1.   Documentación oficial: [PaddleOCR Documentation](https://www.paddleocr.ai/v3.0.0/en/version3.x/pipeline_usage/OCR.html)

+

Decisión

+

Se selecciona PaddleOCR (PP-OCRv5) para la fase de optimización debido a:

+

·     Resultados iniciales prometedores (CER ~5%)

+

·     Alta configurabilidad de hiperparámetros de inferencia

+

·     Pipeline modular que permite experimentación

+

4.1.5. Limitaciones del Benchmark

+

1.   Tamaño reducido: Solo 5 páginas evaluadas en el benchmark comparativo inicial

+

2.   Único tipo de documento: Documentos académicos de UNIR únicamente

+

3.   Ground truth: El texto de referencia se extrajo automáticamente del PDF, lo cual puede introducir errores en layouts complejos

+

4.1.6. Resumen de la Sección

+

Esta sección ha presentado:

+

1.   La configuración del benchmark según ocr_benchmark_notebook.ipynb

+

2.   Los resultados cuantitativos de PaddleOCR del archivo CSV de resultados

+

3.   La justificación de la selección de PaddleOCR para optimización

+

Fuentes de datos utilizadas:

+

·     ocr_benchmark_notebook.ipynb: Código del benchmark

+

·     results/ai_ocr_benchmark_finetune_results_20251206_113206.csv: Resultados numéricos

+

·     Documentación oficial de PaddleOCR

+

4.2. Desarrollo de la comparativa: Optimización de hiperparámetros

+

4.2.1. Introducción

+

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 src/paddle_ocr_fine_tune_unir_raytune.ipynb y los resultados se almacenaron en src/raytune_paddle_subproc_results_20251207_192320.csv.

+

4.2.2. Configuración del Experimento

+

Entorno de Ejecución

+

Según los outputs del notebook:

+

Tabla 11. Tabla 6. Entorno de ejecución del experimento.

+

Componente

Versión/Especificación

Python

3.11.9

PaddlePaddle

3.2.2

PaddleOCR

3.3.2

Ray

2.52.1

GPU

No disponible (CPU only)

+

Fuente: Elaboración propia.

+

 

+

Dataset

+

Se utilizó un dataset estructurado en src/dataset/ creado mediante el notebook src/prepare_dataset.ipynb:

+

·     Estructura: Carpetas con subcarpetas img/ y txt/ pareadas

+

·     Páginas evaluadas por trial: 5 (páginas 5-10 del documento)

+

·     Gestión de datos: Clase ImageTextDataset en src/dataset_manager.py

+

Espacio de Búsqueda

+

Según el código del notebook, se definió el siguiente espacio de búsqueda:

+

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),
+}

+

Descripción de parámetros (según documentación de PaddleOCR):

+

Tabla 12. Tabla de datos.

+

Parámetro

Descripción

use_doc_orientation_classify

Clasificación de orientación del documento

use_doc_unwarping

Corrección de deformación del documento

textline_orientation

Clasificación de orientación de línea de texto

text_det_thresh

Umbral de detección de píxeles de texto

text_det_box_thresh

Umbral de caja de detección

text_det_unclip_ratio

Coeficiente de expansión (fijado en 0.0)

text_rec_score_thresh

Umbral de confianza de reconocimiento

+

Fuente: Elaboración propia.

+

 

+

Configuración de Ray Tune

+

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
+)

+

·     Métrica objetivo: CER (minimizar)

+

·     Algoritmo de búsqueda: Optuna (TPE - Tree-structured Parzen Estimator)

+

·     Número de trials: 64

+

·     Trials concurrentes: 2

+

4.2.3. Resultados de la Optimización

+

Estadísticas Descriptivas

+

Del archivo CSV de resultados (raytune_paddle_subproc_results_20251207_192320.csv):

+

Tabla 13. Tabla 7. Estadísticas descriptivas de los 64 trials de Ray Tune.

+

Estadística

CER

WER

Tiempo (s)

Tiempo/Página (s)

count

64

64

64

64

mean

5.25%

14.28%

347.61

69.42

std

11.03%

10.75%

7.88

1.57

min

1.15%

9.89%

320.97

64.10

25%

1.20%

10.04%

344.24

68.76

50%

1.23%

10.20%

346.42

69.19

75%

4.03%

13.20%

350.14

69.93

max

51.61%

59.45%

368.57

73.63

+

Fuente: Elaboración propia.

+

 

+

Mejor Configuración Encontrada

+

Según el análisis del notebook:

+

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

+

Análisis de Correlación

+

Correlación de Pearson entre parámetros y métricas de error (del notebook):

+

Correlación con CER:

+

Tabla 14. Tabla de datos.

+

Parámetro

Correlación

CER

1.000

config/text_det_box_thresh

0.226

config/text_rec_score_thresh

-0.161

config/text_det_thresh

-0.523

config/text_det_unclip_ratio

NaN

+

Fuente: Elaboración propia.

+

 

+

Correlación con WER:

+

Tabla 15. Tabla de datos.

+

Parámetro

Correlación

WER

1.000

config/text_det_box_thresh

0.227

config/text_rec_score_thresh

-0.173

config/text_det_thresh

-0.521

config/text_det_unclip_ratio

NaN

+

Fuente: Elaboración propia.

+

 

+

Hallazgo clave: El parámetro text_det_thresh muestra la correlación más fuerte (-0.52), indicando que valores más altos de este umbral tienden a reducir el error.

+

Impacto del Parámetro textline_orientation

+

Según el análisis del notebook, este parámetro booleano tiene el mayor impacto:

+

Tabla 16. Tabla 8. Impacto del parámetro textline_orientation en las métricas de error.

+

textline_orientation

CER Medio

WER Medio

True

~3.76%

~12.73%

False

~12.40%

~21.71%

+

Fuente: Elaboración propia.

+

 

+

Interpretación: El CER medio es ~3.3x menor con textline_orientation=True (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.

+

Figura 6. Impacto de textline_orientation en CER

+

Impacto de textline_orientation en CER

+

Fuente: Elaboración propia.

+

 

+

Análisis de Fallos

+

Los trials con CER muy alto (>40%) se produjeron cuando:

+

·     text_det_thresh < 0.1 (valores muy bajos)

+

·     textline_orientation = False

+

Ejemplo de trial con fallo catastrófico:

+

·     CER: 51.61%

+

·     WER: 59.45%

+

·     Configuración: text_det_thresh=0.017, textline_orientation=True

+

4.2.4. Comparación Baseline vs Optimizado

+

Resultados sobre Dataset Completo (24 páginas)

+

Del análisis final del notebook ejecutando sobre las 24 páginas:

+

Tabla 17. Tabla 9. Comparación baseline vs configuración optimizada (24 páginas).

+

Modelo

CER

WER

PaddleOCR (Baseline)

7.78%

14.94%

PaddleOCR-HyperAdjust

1.49%

7.62%

+

Fuente: Elaboración propia.

+

 

+

Métricas de Mejora

+

Tabla 18. Tabla 10. Análisis de la mejora obtenida.

+

Métrica

Baseline

Optimizado

Mejora Absoluta

Reducción Error

CER

7.78%

1.49%

-6.29 pp

80.9%

WER

14.94%

7.62%

-7.32 pp

49.0%

+

Fuente: Elaboración propia.

+

 

+

Interpretación (del notebook)

+

"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%."

+

Figura 7. Comparación Baseline vs Optimizado (24 páginas)

+

Comparación Baseline vs Optimizado (24 páginas)

+

Fuente: Elaboración propia.

+

 

+

Impacto práctico: En un documento de 10,000 caracteres:

+

·     Baseline: ~778 caracteres con error

+

·     Optimizado: ~149 caracteres con error

+

·     Diferencia: ~629 caracteres menos con errores

+

4.2.5. Tiempo de Ejecución

+

Tabla 19. Tabla de datos.

+

Métrica

Valor

Tiempo total del experimento

~6 horas (64 trials × ~6 min/trial)

Tiempo medio por trial

367.72 segundos

Tiempo medio por página

69.42 segundos

Total páginas procesadas

64 trials × 5 páginas = 320 evaluaciones

+

Fuente: Elaboración propia.

+

 

+

4.2.6. Resumen de la Sección

+

Esta sección ha presentado:

+

1.   Configuración del experimento: 64 trials con Ray Tune + Optuna sobre 7 hiperparámetros

+

2.   Resultados estadísticos: CER medio 5.25%, CER mínimo 1.15%

+

3.   Hallazgos clave:

+

- textline_orientation=True es crítico (reduce CER ~70%) - text_det_thresh tiene correlación -0.52 con CER - Valores bajos de text_det_thresh (<0.1) causan fallos catastróficos

+

1.   Mejora final: CER reducido de 7.78% a 1.49% (reducción del 80.9%)

+

Fuentes de datos:

+

·     src/paddle_ocr_fine_tune_unir_raytune.ipynb: Código del experimento

+

·     src/raytune_paddle_subproc_results_20251207_192320.csv: Resultados de 64 trials

+

·     src/paddle_ocr_tuning.py: Script de evaluación

+

4.3. Discusión y análisis de resultados

+

4.3.1. Introducción

+

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.

+

4.3.2. Resumen de Resultados

+

Resultados del Benchmark Comparativo

+

Del archivo results/ai_ocr_benchmark_finetune_results_20251206_113206.csv, PaddleOCR con configuración inicial (use_textline_orientation=True) obtuvo los siguientes resultados en las páginas 5-9:

+

Tabla 20. Tabla de datos.

+

Página

WER

CER

5

12.16%

6.33%

6

12.81%

6.40%

7

11.06%

6.24%

8

8.13%

1.54%

9

10.61%

5.58%

Promedio

10.95%

5.22%

+

Fuente: Elaboración propia.

+

 

+

Resultados de la Optimización con Ray Tune

+

Del archivo src/raytune_paddle_subproc_results_20251207_192320.csv (64 trials):

+

Tabla 21. Tabla de datos.

+

Métrica

Valor

CER mínimo

1.15%

CER medio

5.25%

CER máximo

51.61%

WER mínimo

9.89%

WER medio

14.28%

WER máximo

59.45%

+

Fuente: Elaboración propia.

+

 

+

Comparación Final (Dataset Completo - 24 páginas)

+

Resultados del notebook src/paddle_ocr_fine_tune_unir_raytune.ipynb:

+

Tabla 22. Tabla de datos.

+

Modelo

CER

Precisión Caracteres

WER

Precisión Palabras

PaddleOCR (Baseline)

7.78%

92.22%

14.94%

85.06%

PaddleOCR-HyperAdjust

1.49%

98.51%

7.62%

92.38%

+

Fuente: Elaboración propia.

+

 

+

4.3.3. Análisis de Resultados

+

Mejora Obtenida

+

Tabla 23. Tabla de datos.

+

Forma de Medición

Valor

Mejora en precisión de caracteres (absoluta)

+6.29 puntos porcentuales

Reducción del CER (relativa)

80.9%

Mejora en precisión de palabras (absoluta)

+7.32 puntos porcentuales

Reducción del WER (relativa)

49.0%

Precisión final de caracteres

98.51%

+

Fuente: Elaboración propia.

+

 

+

Impacto de Hiperparámetros Individuales

+

Parámetro textline_orientation

+

Este parámetro booleano demostró ser el más influyente:

+

Tabla 24. Tabla de datos.

+

Valor

CER Medio

Impacto

True

~3.76%

Rendimiento óptimo

False

~12.40%

3.3x peor

+

Fuente: Elaboración propia.

+

 

+

Reducción del CER: 69.7% cuando se habilita la clasificación de orientación de línea.

+

Parámetro text_det_thresh

+

Correlación con CER: -0.523 (la más fuerte de los parámetros continuos)

+

Tabla 25. Tabla de datos.

+

Rango

Comportamiento

< 0.1

Fallos catastróficos (CER 40-50%)

0.3 - 0.6

Rendimiento óptimo

Valor óptimo

0.4690

+

Fuente: Elaboración propia.

+

 

+

Parámetros con menor impacto

+

Tabla 26. Tabla de datos.

+

Parámetro

Correlación con CER

Valor óptimo

text_det_box_thresh

+0.226

0.5412

text_rec_score_thresh

-0.161

0.6350

use_doc_orientation_classify

-

False

use_doc_unwarping

-

False

+

Fuente: Elaboración propia.

+

 

+

Configuración Óptima Final

+

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,
+}

+

4.3.4. Discusión

+

Hallazgos Principales

+

1.   Importancia de la clasificación de orientación de línea: El parámetro textline_orientation=True 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.

+

1.   Umbral de detección crítico: El parámetro text_det_thresh presenta un umbral mínimo efectivo (~0.1). Valores inferiores generan demasiados falsos positivos en la detección, corrompiendo el reconocimiento posterior.

+

1.   Componentes opcionales innecesarios: Para documentos académicos digitales (no escaneados), los módulos de corrección de orientación de documento (use_doc_orientation_classify) y corrección de deformación (use_doc_unwarping) no aportan mejora e incluso pueden introducir overhead.

+

Interpretación de la Correlación Negativa

+

La correlación negativa de text_det_thresh (-0.52) con el CER indica que:

+

·     Umbrales más altos filtran detecciones de baja confianza

+

·     Esto reduce falsos positivos que generan texto erróneo

+

·     El reconocimiento es más preciso con menos regiones pero más confiables

+

Limitaciones de los Resultados

+

1.   Generalización: Los resultados se obtuvieron sobre documentos de un único tipo (instrucciones académicas UNIR). La configuración óptima puede variar para otros tipos de documentos.

+

1.   Ground truth automático: El texto de referencia se extrajo programáticamente del PDF. En layouts complejos, esto puede introducir errores en la evaluación.

+

1.   Ejecución en CPU: Los tiempos reportados (~69s/página) corresponden a ejecución en CPU. Con GPU, los tiempos serían significativamente menores.

+

1.   Parámetro fijo: text_det_unclip_ratio permaneció fijo en 0.0 durante todo el experimento por decisión de diseño.

+

Comparación con Objetivos

+

Tabla 27. Tabla de datos.

+

Objetivo

Meta

Resultado

Cumplimiento

OE1: Comparar soluciones OCR

Evaluar EasyOCR, PaddleOCR, DocTR

PaddleOCR seleccionado

✓

OE2: Preparar dataset

Construir dataset estructurado

Dataset de 24 páginas

✓

OE3: Identificar hiperparámetros críticos

Analizar correlaciones

textline_orientation y text_det_thresh identificados

✓

OE4: Optimizar con Ray Tune

Mínimo 50 configuraciones

64 trials ejecutados

✓

OE5: Validar configuración

Documentar mejora

CER 7.78% → 1.49%

✓

Objetivo General

CER < 2%

CER = 1.49%

✓

+

Fuente: Elaboración propia.

+

 

+

4.3.5. Implicaciones Prácticas

+

Recomendaciones de Configuración

+

Para documentos académicos en español similares a los evaluados:

+

1.   Obligatorio: use_textline_orientation=True

+

2.   Recomendado: text_det_thresh entre 0.4 y 0.5

+

3.   Opcional: text_det_box_thresh ~0.5, text_rec_score_thresh >0.6

+

4.   No recomendado: Habilitar use_doc_orientation_classify o use_doc_unwarping para documentos digitales

+

Impacto Cuantitativo

+

En un documento típico de 10,000 caracteres:

+

Tabla 28. Tabla de datos.

+

Configuración

Errores estimados

Baseline

~778 caracteres

Optimizada

~149 caracteres

Reducción

629 caracteres menos con errores

+

Fuente: Elaboración propia.

+

 

+

Aplicabilidad

+

Esta metodología de optimización es aplicable cuando:

+

·     No se dispone de recursos GPU para fine-tuning

+

·     El modelo preentrenado ya tiene soporte para el idioma objetivo

+

·     Se busca mejorar rendimiento sin reentrenar

+

4.3.6. Resumen de la Sección

+

Esta sección ha presentado:

+

1.   Los resultados consolidados del benchmark y la optimización

+

2.   El análisis del impacto de cada hiperparámetro

+

3.   La configuración óptima identificada

+

4.   La discusión de limitaciones y aplicabilidad

+

5.   El cumplimiento de los objetivos planteados

+

Resultado principal: 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 < 2%.

+

Fuentes de datos:

+

·     results/ai_ocr_benchmark_finetune_results_20251206_113206.csv

+

·     src/raytune_paddle_subproc_results_20251207_192320.csv

+

·     src/paddle_ocr_fine_tune_unir_raytune.ipynb

5.   +Conclusiones +y trabajo futuro

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.

+

5.1. Conclusiones

+

5.1.1. Conclusiones Generales

+

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.

+

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:

+

Tabla 29. Tabla de datos.

+

Métrica

Objetivo

Resultado

CER

< 2%

1.49%

+

Fuente: Elaboración propia.

+

 

+

5.1.2. Conclusiones Específicas

+

Respecto a OE1 (Comparativa de soluciones OCR):

+

·     Se evaluaron tres soluciones OCR de código abierto: EasyOCR, PaddleOCR (PP-OCRv5) y DocTR

+

·     PaddleOCR demostró el mejor rendimiento base para documentos en español

+

·     La configurabilidad del pipeline de PaddleOCR lo hace idóneo para optimización

+

Respecto a OE2 (Preparación del dataset):

+

·     Se construyó un dataset estructurado con 24 páginas de documentos académicos

+

·     La clase ImageTextDataset facilita la carga de pares imagen-texto

+

·     El ground truth se extrajo automáticamente del PDF mediante PyMuPDF

+

Respecto a OE3 (Identificación de hiperparámetros críticos):

+

·     El parámetro textline_orientation es el más influyente: reduce el CER en un 69.7% cuando está habilitado

+

·     El umbral text_det_thresh presenta la correlación más fuerte (-0.52) con el CER

+

·     Los parámetros de corrección de documento (use_doc_orientation_classify, use_doc_unwarping) no aportan mejora en documentos digitales

+

Respecto a OE4 (Optimización con Ray Tune):

+

·     Se ejecutaron 64 trials con el algoritmo OptunaSearch

+

·     El tiempo total del experimento fue aproximadamente 6 horas (en CPU)

+

·     La arquitectura basada en subprocesos permitió superar incompatibilidades entre Ray y PaddleOCR

+

Respecto a OE5 (Validación de la configuración):

+

·     Se validó la configuración óptima sobre el dataset completo de 24 páginas

+

·     La mejora obtenida fue del 80.9% en reducción del CER (7.78% → 1.49%)

+

·     La precisión de caracteres alcanzó el 98.51%

+

5.1.3. Hallazgos Clave

+

1.   Arquitectura sobre umbrales: Un único parámetro booleano (textline_orientation) tiene más impacto que todos los umbrales continuos combinados.

+

1.   Umbrales mínimos efectivos: Valores de text_det_thresh < 0.1 causan fallos catastróficos (CER >40%).

+

1.   Simplicidad para documentos digitales: Para documentos PDF digitales (no escaneados), los módulos de corrección de orientación y deformación son innecesarios.

+

1.   Optimización sin fine-tuning: Se puede mejorar significativamente el rendimiento de modelos preentrenados mediante ajuste de hiperparámetros de inferencia.

+

5.1.4. Contribuciones del Trabajo

+

1.   Metodología reproducible: Se documenta un proceso completo de optimización de hiperparámetros OCR con Ray Tune + Optuna.

+

1.   Análisis de hiperparámetros de PaddleOCR: Se cuantifica el impacto de cada parámetro configurable mediante correlaciones y análisis comparativo.

+

1.   Configuración óptima para español: Se proporciona una configuración validada para documentos académicos en español.

+

1.   Código fuente: Todo el código está disponible en el repositorio GitHub para reproducción y extensión.

+

5.1.5. Limitaciones del Trabajo

+

1.   Tipo de documento único: Los experimentos se realizaron únicamente sobre documentos académicos de UNIR. La generalización a otros tipos de documentos requiere validación adicional.

+

1.   Tamaño del dataset: 24 páginas es un corpus limitado para conclusiones estadísticamente robustas.

+

1.   Ground truth automático: La extracción automática del texto de referencia puede introducir errores en layouts complejos.

+

1.   Ejecución en CPU: Los tiempos de procesamiento (~69s/página) limitan la aplicabilidad en escenarios de alto volumen.

+

1.   Parámetro no explorado: text_det_unclip_ratio permaneció fijo en 0.0 durante todo el experimento.

+

5.2. Líneas de trabajo futuro

+

5.2.1. Extensiones Inmediatas

+

1.   Validación cruzada: Evaluar la configuración óptima en otros tipos de documentos en español (facturas, formularios, textos manuscritos).

+

1.   Exploración de text_det_unclip_ratio: Incluir este parámetro en el espacio de búsqueda.

+

1.   Dataset ampliado: Construir un corpus más amplio y diverso de documentos en español.

+

1.   Evaluación con GPU: Medir tiempos de inferencia con aceleración GPU.

+

5.2.2. Líneas de Investigación

+

1.   Transfer learning de hiperparámetros: Investigar si las configuraciones óptimas para un tipo de documento transfieren a otros dominios.

+

1.   Optimización multi-objetivo: Considerar simultáneamente CER, WER y tiempo de inferencia como objetivos.

+

1.   AutoML para OCR: Aplicar técnicas de AutoML más avanzadas (Neural Architecture Search, meta-learning).

+

1.   Comparación con fine-tuning: Cuantificar la brecha de rendimiento entre optimización de hiperparámetros y fine-tuning real.

+

5.2.3. Aplicaciones Prácticas

+

1.   Herramienta de configuración automática: Desarrollar una herramienta que determine automáticamente la configuración óptima para un nuevo tipo de documento.

+

1.   Integración en pipelines de producción: Implementar la configuración optimizada en sistemas reales de procesamiento documental.

+

1.   Benchmark público: Publicar un benchmark de OCR para documentos en español que facilite la comparación de soluciones.

+

5.2.4. Reflexión Final

+

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.

+

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.

+

El código fuente y los datos experimentales están disponibles públicamente para facilitar la reproducción y extensión de este trabajo.

Referencias +bibliográficas

Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2623-2631. https://doi.org/10.1145/3292500.3330701

+

Baek, Y., Lee, B., Han, D., Yun, S., & Lee, H. (2019). Character region awareness for text detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9365-9374. https://doi.org/10.1109/CVPR.2019.00959

+

Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(1), 281-305. https://jmlr.org/papers/v13/bergstra12a.html

+

Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems, 24, 2546-2554. https://papers.nips.cc/paper/2011/hash/86e8f7ab32cfd12577bc2619bc635690-Abstract.html

+

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

+

Doran, G. T. (1981). There's a S.M.A.R.T. way to write management's goals and objectives. Management Review, 70(11), 35-36.

+

Du, Y., Li, C., Guo, R., Yin, X., Liu, W., Zhou, J., Bai, Y., Yu, Z., Yang, Y., Dang, Q., & Wang, H. (2020). PP-OCR: A practical ultra lightweight OCR system. arXiv preprint arXiv:2009.09941. https://arxiv.org/abs/2009.09941

+

Du, Y., Li, C., Guo, R., Cui, C., Liu, W., Zhou, J., Lu, B., Yang, Y., Liu, Q., Hu, X., Yu, D., & Wang, H. (2023). PP-OCRv4: Mobile scene text detection and recognition. arXiv preprint arXiv:2310.05930. https://arxiv.org/abs/2310.05930

+

Feurer, M., & Hutter, F. (2019). Hyperparameter optimization. In F. Hutter, L. Kotthoff, & J. Vanschoren (Eds.), Automated machine learning: Methods, systems, challenges (pp. 3-33). Springer. https://doi.org/10.1007/978-3-030-05318-5_1

+

He, P., Huang, W., Qiao, Y., Loy, C. C., & Tang, X. (2016). Reading scene text in deep convolutional sequences. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1), 3501-3508. https://doi.org/10.1609/aaai.v30i1.10291

+

JaidedAI. (2020). EasyOCR: Ready-to-use OCR with 80+ supported languages. GitHub. https://github.com/JaidedAI/EasyOCR

+

Liang, J., Doermann, D., & Li, H. (2005). Camera-based analysis of text and documents: A survey. International Journal of Document Analysis and Recognition, 7(2), 84-104. https://doi.org/10.1007/s10032-004-0138-z

+

Liao, M., Wan, Z., Yao, C., Chen, K., & Bai, X. (2020). Real-time scene text detection with differentiable binarization. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11474-11481. https://doi.org/10.1609/aaai.v34i07.6812

+

Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J. E., & Stoica, I. (2018). Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118. https://arxiv.org/abs/1807.05118

+

Mindee. (2021). DocTR: Document Text Recognition. GitHub. https://github.com/mindee/doctr

+

Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., Elibol, M., Yang, Z., Paul, W., Jordan, M. I., & Stoica, I. (2018). Ray: A distributed framework for emerging AI applications. 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), 561-577. https://www.usenix.org/conference/osdi18/presentation/moritz

+

Morris, A. C., Maier, V., & Green, P. D. (2004). From WER and RIL to MER and WIL: Improved evaluation measures for connected speech recognition. Eighth International Conference on Spoken Language Processing. https://doi.org/10.21437/Interspeech.2004-668

+

PaddlePaddle. (2024). PaddleOCR: Awesome multilingual OCR toolkits based on PaddlePaddle. GitHub. https://github.com/PaddlePaddle/PaddleOCR

+

Pearson, K. (1895). Notes on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London, 58, 240-242. https://doi.org/10.1098/rspl.1895.0041

+

PyMuPDF. (2024). PyMuPDF documentation. https://pymupdf.readthedocs.io/

+

Shi, B., Bai, X., & Yao, C. (2016). An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2298-2304. https://doi.org/10.1109/TPAMI.2016.2646371

+

Smith, R. (2007). An overview of the Tesseract OCR engine. Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), 2, 629-633. https://doi.org/10.1109/ICDAR.2007.4376991

+

Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., & Liang, J. (2017). EAST: An efficient and accurate scene text detector. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5551-5560. https://doi.org/10.1109/CVPR.2017.283

+

Zoph, B., & Le, Q. V. (2017). Neural architecture search with reinforcement learning. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1611.01578

+

Anexo A.    +Código fuente y datos analizados

A.1 Repositorio del Proyecto

+

El código fuente completo y los datos utilizados en este trabajo están disponibles en el siguiente repositorio:

+

URL del repositorio: https://github.com/seryus/MastersThesis

+

El repositorio incluye:

+

·     Notebooks de experimentación: Código completo de los experimentos realizados

+

·     Scripts de evaluación: Herramientas para evaluar modelos OCR

+

·     Dataset: Imágenes y textos de referencia utilizados

+

·     Resultados: Archivos CSV con los resultados de los 64 trials de Ray Tune

+

A.2 Estructura del Repositorio

+

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

+

A.3 Requisitos de Software

+

Para reproducir los experimentos se requieren las siguientes dependencias:

+

Tabla 30. Tabla de datos.

+

Componente

Versión

Python

3.11.9

PaddlePaddle

3.2.2

PaddleOCR

3.3.2

Ray

2.52.1

Optuna

4.6.0

jiwer

(última versión)

PyMuPDF

(última versión)

+

Fuente: Elaboración propia.

+

 

+

A.4 Instrucciones de Ejecución

+

1.   Clonar el repositorio

+

2.   Instalar dependencias: pip install -r requirements.txt

+

3.   Ejecutar el notebook src/paddle_ocr_fine_tune_unir_raytune.ipynb

+

A.5 Licencia

+

El código se distribuye bajo licencia MIT.

+

+
+ +
+ +
+
+ + diff --git a/thesis_output_files/colorschememapping.xml b/thesis_output/plantilla_individual_files/colorschememapping.xml similarity index 100% rename from thesis_output_files/colorschememapping.xml rename to thesis_output/plantilla_individual_files/colorschememapping.xml diff --git a/thesis_output_files/filelist.xml b/thesis_output/plantilla_individual_files/filelist.xml similarity index 100% rename from thesis_output_files/filelist.xml rename to thesis_output/plantilla_individual_files/filelist.xml diff --git a/thesis_output_files/header.htm b/thesis_output/plantilla_individual_files/header.htm similarity index 100% rename from thesis_output_files/header.htm rename to thesis_output/plantilla_individual_files/header.htm diff --git a/thesis_output_files/image001.png b/thesis_output/plantilla_individual_files/image001.png similarity index 100% rename from thesis_output_files/image001.png rename to thesis_output/plantilla_individual_files/image001.png diff --git a/thesis_output_files/image002.gif b/thesis_output/plantilla_individual_files/image002.gif similarity index 100% rename from thesis_output_files/image002.gif rename to thesis_output/plantilla_individual_files/image002.gif diff --git a/thesis_output_files/image003.png b/thesis_output/plantilla_individual_files/image003.png similarity index 100% rename from thesis_output_files/image003.png rename to thesis_output/plantilla_individual_files/image003.png diff --git a/thesis_output_files/image004.jpg b/thesis_output/plantilla_individual_files/image004.jpg similarity index 100% rename from thesis_output_files/image004.jpg rename to thesis_output/plantilla_individual_files/image004.jpg diff --git a/thesis_output_files/image005.png b/thesis_output/plantilla_individual_files/image005.png similarity index 100% rename from thesis_output_files/image005.png rename to thesis_output/plantilla_individual_files/image005.png diff --git a/thesis_output_files/image006.gif b/thesis_output/plantilla_individual_files/image006.gif similarity index 100% rename from thesis_output_files/image006.gif rename to thesis_output/plantilla_individual_files/image006.gif diff --git a/thesis_output_files/item0001.xml b/thesis_output/plantilla_individual_files/item0001.xml similarity index 100% rename from thesis_output_files/item0001.xml rename to thesis_output/plantilla_individual_files/item0001.xml diff --git a/thesis_output_files/item0003.xml b/thesis_output/plantilla_individual_files/item0003.xml similarity index 100% rename from thesis_output_files/item0003.xml rename to thesis_output/plantilla_individual_files/item0003.xml diff --git a/thesis_output_files/item0005.xml b/thesis_output/plantilla_individual_files/item0005.xml similarity index 100% rename from thesis_output_files/item0005.xml rename to thesis_output/plantilla_individual_files/item0005.xml diff --git a/thesis_output_files/item0007.xml b/thesis_output/plantilla_individual_files/item0007.xml similarity index 100% rename from thesis_output_files/item0007.xml rename to thesis_output/plantilla_individual_files/item0007.xml diff --git a/thesis_output_files/props002.xml b/thesis_output/plantilla_individual_files/props002.xml similarity index 100% rename from thesis_output_files/props002.xml rename to thesis_output/plantilla_individual_files/props002.xml diff --git a/thesis_output_files/props004.xml b/thesis_output/plantilla_individual_files/props004.xml similarity index 100% rename from thesis_output_files/props004.xml rename to thesis_output/plantilla_individual_files/props004.xml diff --git a/thesis_output_files/props006.xml b/thesis_output/plantilla_individual_files/props006.xml similarity index 100% rename from thesis_output_files/props006.xml rename to thesis_output/plantilla_individual_files/props006.xml diff --git a/thesis_output_files/props008.xml b/thesis_output/plantilla_individual_files/props008.xml similarity index 100% rename from thesis_output_files/props008.xml rename to thesis_output/plantilla_individual_files/props008.xml diff --git a/thesis_output_files/themedata.thmx b/thesis_output/plantilla_individual_files/themedata.thmx similarity index 100% rename from thesis_output_files/themedata.thmx rename to thesis_output/plantilla_individual_files/themedata.thmx