1130 lines
774 KiB
Plaintext
1130 lines
774 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "be3c1872",
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"metadata": {},
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"source": [
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"# AI-based OCR Benchmark Notebook\n",
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"\n",
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"This notebook benchmarks **AI-based OCR models** on scanned PDF documents/images in Spanish.\n",
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"It excludes traditional OCR engines like Tesseract that require external installations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "6a1e98fe",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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"Requirement already satisfied: pip in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (25.3)\n",
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"Note: you may need to restart the kernel to use updated packages.\n",
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"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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"Requirement already satisfied: jupyter in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (1.1.1)\n",
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"Requirement already satisfied: notebook in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyter) (7.4.7)\n",
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"Requirement already satisfied: jupyter-console in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyter) (6.6.3)\n",
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"Requirement already satisfied: nbconvert in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyter) (7.16.6)\n",
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"Requirement already satisfied: ipykernel in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyter) (7.1.0)\n",
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"Requirement already satisfied: ipywidgets in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyter) (8.1.8)\n",
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"Requirement already satisfied: comm>=0.1.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ipykernel->jupyter) (0.2.3)\n",
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"Requirement already satisfied: debugpy>=1.6.5 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ipykernel->jupyter) (1.8.17)\n",
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"Requirement already satisfied: ipython>=7.23.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ipykernel->jupyter) (9.6.0)\n",
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"Requirement already satisfied: matplotlib-inline>=0.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ipykernel->jupyter) (0.1.7)\n",
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"Requirement already satisfied: nest-asyncio>=1.4 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ipykernel->jupyter) (1.6.0)\n",
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"Requirement already satisfied: psutil>=5.7 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ipykernel->jupyter) (7.1.0)\n",
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"Requirement already satisfied: pyzmq>=25 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ipykernel->jupyter) (27.1.0)\n",
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"Requirement already satisfied: tornado>=6.2 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ipykernel->jupyter) (6.5.2)\n",
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"Requirement already satisfied: wcwidth in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from prompt_toolkit<3.1.0,>=3.0.41->ipython>=7.23.1->ipykernel->jupyter) (0.2.14)\n",
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"Requirement already satisfied: parso<0.9.0,>=0.8.4 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jedi>=0.16->ipython>=7.23.1->ipykernel->jupyter) (0.8.5)\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyter-client>=8.0.0->ipykernel->jupyter) (2.9.0.post0)\n",
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"Requirement already satisfied: jupyter-lsp>=2.0.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyterlab->jupyter) (2.3.0)\n",
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"Requirement already satisfied: notebook-shim>=0.2 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyterlab->jupyter) (0.2.4)\n",
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"Requirement already satisfied: anyio in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from httpx<1,>=0.25.0->jupyterlab->jupyter) (4.11.0)\n",
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"Requirement already satisfied: certifi in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from httpx<1,>=0.25.0->jupyterlab->jupyter) (2025.10.5)\n",
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"Requirement already satisfied: httpcore==1.* in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from httpx<1,>=0.25.0->jupyterlab->jupyter) (1.0.9)\n",
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"Requirement already satisfied: h11>=0.16 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from httpcore==1.*->httpx<1,>=0.25.0->jupyterlab->jupyter) (0.16.0)\n",
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"Requirement already satisfied: prometheus-client>=0.9 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyter-server<3,>=2.4.0->jupyterlab->jupyter) (0.23.1)\n",
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"Requirement already satisfied: babel>=2.10 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyterlab-server<3,>=2.27.1->jupyterlab->jupyter) (2.17.0)\n",
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"Requirement already satisfied: types-python-dateutil>=2.8.10 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from arrow>=0.15.0->isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter) (2.9.0.20251008)\n",
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"Note: you may need to restart the kernel to use updated packages.\n",
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"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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"Note: you may need to restart the kernel to use updated packages.\n",
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"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jupyter-client>=8.0.0->ipykernel) (2.9.0.post0)\n",
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"Requirement already satisfied: six>=1.5 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->jupyter-client>=8.0.0->ipykernel) (1.17.0)\n",
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"Requirement already satisfied: executing>=1.2.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from stack_data->ipython>=7.23.1->ipykernel) (2.2.1)\n",
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"Requirement already satisfied: asttokens>=2.1.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from stack_data->ipython>=7.23.1->ipykernel) (3.0.0)\n",
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"Note: you may need to restart the kernel to use updated packages.\n",
|
||
"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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"Requirement already satisfied: easyocr in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (1.7.2)\n",
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"Requirement already satisfied: torchvision>=0.5 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from easyocr) (0.23.0+cpu)\n",
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"Requirement already satisfied: opencv-python-headless in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from easyocr) (4.12.0.88)\n",
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"Requirement already satisfied: scipy in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from easyocr) (1.16.2)\n",
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"Requirement already satisfied: numpy in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from easyocr) (2.1.2)\n",
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"Requirement already satisfied: python-bidi in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from easyocr) (0.6.6)\n",
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"Requirement already satisfied: PyYAML in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from easyocr) (6.0.2)\n",
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"Requirement already satisfied: Shapely in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from easyocr) (2.1.2)\n",
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"Requirement already satisfied: pyclipper in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from easyocr) (1.3.0.post6)\n",
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"Requirement already satisfied: ninja in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from easyocr) (1.13.0)\n",
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"Requirement already satisfied: filelock in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (3.13.1)\n",
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"Requirement already satisfied: packaging>=20.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (25.0)\n",
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"Requirement already satisfied: regex!=2019.12.17 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (2025.9.18)\n",
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"Requirement already satisfied: safetensors>=0.4.3 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from transformers) (0.6.2)\n",
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"Requirement already satisfied: fsspec>=2023.5.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (2024.6.1)\n",
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"Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (4.15.0)\n",
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"Requirement already satisfied: sympy>=1.13.3 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from torch) (1.13.3)\n",
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"Requirement already satisfied: networkx in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from torch) (3.3)\n",
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"Requirement already satisfied: jinja2 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from torch) (3.1.6)\n",
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"Requirement already satisfied: setuptools in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from torch) (80.9.0)\n",
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"Requirement already satisfied: click>=8.1.8 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jiwer) (8.3.0)\n",
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"Requirement already satisfied: rapidfuzz>=3.9.7 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jiwer) (3.14.1)\n",
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"Requirement already satisfied: paddlex<3.3.0,>=3.2.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (3.2.1)\n",
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"Requirement already satisfied: aistudio_sdk>=0.3.5 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (0.3.8)\n",
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"Requirement already satisfied: chardet in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (5.2.0)\n",
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"Requirement already satisfied: colorlog in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (6.9.0)\n",
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"Requirement already satisfied: modelscope>=1.28.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (1.30.0)\n",
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"Requirement already satisfied: pandas>=1.3 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (2.3.3)\n",
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"Requirement already satisfied: prettytable in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (3.16.0)\n",
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"Requirement already satisfied: py-cpuinfo in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (9.0.0)\n",
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"Requirement already satisfied: pydantic>=2 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (2.12.0)\n",
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"Requirement already satisfied: ruamel.yaml in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (0.18.15)\n",
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"Requirement already satisfied: ujson in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (5.11.0)\n",
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"Requirement already satisfied: imagesize in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (1.4.1)\n",
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"Requirement already satisfied: opencv-contrib-python==4.10.0.84 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (4.10.0.84)\n",
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"Requirement already satisfied: pypdfium2>=4 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (4.30.0)\n",
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"Requirement already satisfied: httpx in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlepaddle) (0.28.1)\n",
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"Requirement already satisfied: protobuf>=3.20.2 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from paddlepaddle) (6.32.1)\n",
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"Requirement already satisfied: h5py<4.0.0,>=3.1.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-doctr) (3.14.0)\n",
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"Requirement already satisfied: opencv-python<5.0.0,>=4.5.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-doctr) (4.12.0.88)\n",
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"Requirement already satisfied: langdetect<2.0.0,>=1.0.9 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-doctr) (1.0.9)\n",
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"Requirement already satisfied: validators>=0.18.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-doctr) (0.35.0)\n",
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"Requirement already satisfied: six in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from langdetect<2.0.0,>=1.0.9->python-doctr) (1.17.0)\n",
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"Requirement already satisfied: ml_dtypes>=0.5.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from onnx<3.0.0,>=1.12.0->python-doctr) (0.5.3)\n",
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"Requirement already satisfied: psutil in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from aistudio_sdk>=0.3.5->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (7.1.0)\n",
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"Requirement already satisfied: bce-python-sdk in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from aistudio_sdk>=0.3.5->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (0.9.46)\n",
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"Requirement already satisfied: colorama in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from click>=8.1.8->jiwer) (0.4.6)\n",
|
||
"Requirement already satisfied: urllib3>=1.26 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from modelscope>=1.28.0->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (2.5.0)\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas>=1.3->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (2.9.0.post0)\n",
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||
"Requirement already satisfied: pytz>=2020.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas>=1.3->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (2025.2)\n",
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"Requirement already satisfied: tzdata>=2022.7 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas>=1.3->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (2025.2)\n",
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"Requirement already satisfied: annotated-types>=0.6.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pydantic>=2->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (0.7.0)\n",
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"Requirement already satisfied: pydantic-core==2.41.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pydantic>=2->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (2.41.1)\n",
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"Requirement already satisfied: typing-inspection>=0.4.2 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pydantic>=2->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (0.4.2)\n",
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"Requirement already satisfied: charset_normalizer<4,>=2 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from requests->transformers) (3.4.3)\n",
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"Requirement already satisfied: pycryptodome>=3.8.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from bce-python-sdk->aistudio_sdk>=0.3.5->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (3.23.0)\n",
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"Requirement already satisfied: future>=0.6.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from bce-python-sdk->aistudio_sdk>=0.3.5->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (1.0.0)\n",
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"Requirement already satisfied: anyio in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from httpx->paddlepaddle) (4.11.0)\n",
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"Requirement already satisfied: httpcore==1.* in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from httpx->paddlepaddle) (1.0.9)\n",
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"Requirement already satisfied: h11>=0.16 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from httpcore==1.*->httpx->paddlepaddle) (0.16.0)\n",
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||
"Requirement already satisfied: sniffio>=1.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from anyio->httpx->paddlepaddle) (1.3.1)\n",
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"Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from jinja2->torch) (3.0.3)\n",
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"Requirement already satisfied: wcwidth in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from prettytable->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (0.2.14)\n",
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"Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from ruamel.yaml->paddlex<3.3.0,>=3.2.0->paddlex[ocr-core]<3.3.0,>=3.2.0->paddleocr) (0.2.14)\n",
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||
"Requirement already satisfied: imageio!=2.35.0,>=2.33 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from scikit-image->easyocr) (2.37.0)\n",
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||
"Requirement already satisfied: tifffile>=2022.8.12 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from scikit-image->easyocr) (2025.10.4)\n",
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"Requirement already satisfied: lazy-loader>=0.4 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from scikit-image->easyocr) (0.4)\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n",
|
||
"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
|
||
"Requirement already satisfied: PyMuPDF in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (1.26.4)\n",
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||
"Note: you may need to restart the kernel to use updated packages.\n",
|
||
"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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||
"Requirement already satisfied: pandas in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (2.3.3)\n",
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"Requirement already satisfied: numpy>=1.26.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas) (2.1.2)\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas) (2.9.0.post0)\n",
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||
"Requirement already satisfied: pytz>=2020.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas) (2025.2)\n",
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"Requirement already satisfied: tzdata>=2022.7 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas) (2025.2)\n",
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"Requirement already satisfied: six>=1.5 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n",
|
||
"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
|
||
"Requirement already satisfied: matplotlib in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (3.10.6)\n",
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"Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (1.3.3)\n",
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"Requirement already satisfied: cycler>=0.10 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (0.12.1)\n",
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"Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (4.60.1)\n",
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"Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (1.4.9)\n",
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"Requirement already satisfied: numpy>=1.23 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (2.1.2)\n",
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"Requirement already satisfied: packaging>=20.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (25.0)\n",
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"Requirement already satisfied: pillow>=8 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (11.3.0)\n",
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"Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (3.2.5)\n",
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"Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib) (2.9.0.post0)\n",
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"Requirement already satisfied: six>=1.5 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n",
|
||
"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
|
||
"Requirement already satisfied: seaborn in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (0.13.2)\n",
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"Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from seaborn) (2.1.2)\n",
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"Requirement already satisfied: pandas>=1.2 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from seaborn) (2.3.3)\n",
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||
"Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from seaborn) (3.10.6)\n",
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||
"Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.3)\n",
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"Requirement already satisfied: cycler>=0.10 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
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"Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.60.1)\n",
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"Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.9)\n",
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||
"Requirement already satisfied: packaging>=20.0 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (25.0)\n",
|
||
"Requirement already satisfied: pillow>=8 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (11.3.0)\n",
|
||
"Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.5)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
|
||
"Requirement already satisfied: tzdata>=2022.7 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
|
||
"Requirement already satisfied: six>=1.5 in c:\\users\\sji\\desktop\\mastersthesis\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%pip install --upgrade pip\n",
|
||
"%pip install --upgrade jupyter\n",
|
||
"%pip install --upgrade ipywidgets\n",
|
||
"%pip install --upgrade ipykernel\n",
|
||
"\n",
|
||
"# Install necessary packages\n",
|
||
"%pip install easyocr transformers torch pdf2image pillow jiwer paddleocr hf_xet paddlepaddle python-doctr\n",
|
||
"# pdf reading\n",
|
||
"%pip install PyMuPDF\n",
|
||
"\n",
|
||
"# Data analysis and visualization\n",
|
||
"%pip install pandas\n",
|
||
"%pip install matplotlib\n",
|
||
"%pip install seaborn"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "ae33632a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Imports\n",
|
||
"import os\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from pdf2image import convert_from_path\n",
|
||
"from PIL import Image, ImageOps\n",
|
||
"import easyocr\n",
|
||
"from transformers import TrOCRProcessor, VisionEncoderDecoderModel\n",
|
||
"import torch\n",
|
||
"from jiwer import wer, cer\n",
|
||
"from paddleocr import PaddleOCR\n",
|
||
"import fitz # PyMuPDF\n",
|
||
"from doctr.models import ocr_predictor\n",
|
||
"import re"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0e00f1b0",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 1 Configuration"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"PDF_FOLDER = './instructions' # Folder containing PDF files\n",
|
||
"OUTPUT_FOLDER = 'results'\n",
|
||
"os.makedirs(OUTPUT_FOLDER, exist_ok=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "dcefbebc",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[2025-11-10 16:39:09,601] [ WARNING] easyocr.py:80 - Neither CUDA nor MPS are available - defaulting to CPU. Note: This module is much faster with a GPU.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 1. EasyOCR \n",
|
||
"import easyocr\n",
|
||
"easyocr_reader = easyocr.Reader(['es', 'en']) # Spanish and English"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "243849b9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[32mCreating model: ('PP-LCNet_x1_0_textline_ori', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\PP-LCNet_x1_0_textline_ori`.\u001b[0m\n",
|
||
"\u001b[32mCreating model: ('PP-OCRv5_server_det', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\PP-OCRv5_server_det`.\u001b[0m\n",
|
||
"\u001b[32mCreating model: ('PP-OCRv5_server_rec', None)\u001b[0m\n",
|
||
"\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\sji\\.paddlex\\official_models\\PP-OCRv5_server_rec`.\u001b[0m\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 3. PaddleOCR \n",
|
||
"# https://www.paddleocr.ai/v3.0.0/en/version3.x/pipeline_usage/OCR.html?utm_source=chatgpt.com#21-command-line\n",
|
||
"from paddleocr import PaddleOCR\n",
|
||
"\n",
|
||
"# Initialize with better settings for Spanish/Latin text\n",
|
||
"# https://www.paddleocr.ai/main/en/version3.x/algorithm/PP-OCRv5/PP-OCRv5_multi_languages.html?utm_source=chatgpt.com#5-models-and-their-supported-languages\n",
|
||
"paddleocr_model = PaddleOCR(\n",
|
||
" text_detection_model_name=\"PP-OCRv5_server_det\",\n",
|
||
" text_recognition_model_name=\"PP-OCRv5_server_rec\",\n",
|
||
" use_doc_orientation_classify=False,\n",
|
||
" use_doc_unwarping=False,\n",
|
||
" use_textline_orientation=True,\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "329da34a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"3.2.0\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import paddleocr\n",
|
||
"\n",
|
||
"print(paddleocr.__version__)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "f5b30d2f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# doctr\n",
|
||
"doctr_model = ocr_predictor(det_arch=\"db_resnet50\", reco_arch=\"sar_resnet31\", pretrained=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "84c999e2",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 2 Helper Functions"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "9596c7df",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from typing import List, Optional\n",
|
||
"\n",
|
||
"def show_page(img: Image.Image, text: str, scale: float = 0.15):\n",
|
||
" \"\"\"\n",
|
||
" Displays a smaller version of the image with text as a footer.\n",
|
||
" \"\"\"\n",
|
||
" # Compute plot size based on image dimensions (but without resizing the image)\n",
|
||
" w, h = img.size\n",
|
||
" figsize = (w * scale / 100, h * scale / 100) # convert pixels to inches approx\n",
|
||
"\n",
|
||
" fig, ax = plt.subplots(figsize=figsize)\n",
|
||
" ax.imshow(img)\n",
|
||
" ax.axis(\"off\")\n",
|
||
"\n",
|
||
"\n",
|
||
" # Add OCR text below the image (footer)\n",
|
||
" plt.figtext(0.5, 0.02, text.strip(), wrap=True, ha='center', va='bottom', fontsize=10)\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"def pdf_to_images(pdf_path: str, dpi: int = 300, pages: List[int] = None) -> List[Image.Image]:\n",
|
||
" \"\"\"Render a PDF into a list of PIL Images using PyMuPDF or pdf2image.\"\"\"\n",
|
||
" images = []\n",
|
||
" if fitz is not None:\n",
|
||
" doc = fitz.open(pdf_path)\n",
|
||
" page_indices = pages if pages is not None else list(range(len(doc)))\n",
|
||
" for i in page_indices:\n",
|
||
" page = doc.load_page(i)\n",
|
||
" mat = fitz.Matrix(dpi/72.0, dpi/72.0)\n",
|
||
" pix = page.get_pixmap(matrix=mat, alpha=False)\n",
|
||
" img = Image.frombytes('RGB', [pix.width, pix.height], pix.samples)\n",
|
||
" images.append(img)\n",
|
||
" doc.close()\n",
|
||
" elif convert_from_path is not None:\n",
|
||
" if pages is None:\n",
|
||
" images = convert_from_path(pdf_path, dpi=dpi)\n",
|
||
" else:\n",
|
||
" # pdf2image supports first_page/last_page; render a slice if contiguous\n",
|
||
" images = [convert_from_path(pdf_path, dpi=dpi)[i] for i in pages]\n",
|
||
" else:\n",
|
||
" raise RuntimeError('Install PyMuPDF or pdf2image to convert PDFs.')\n",
|
||
" return images\n",
|
||
"\n",
|
||
"def pdf_extract_text(pdf_path, page_num, line_tolerance=15) -> str:\n",
|
||
" \"\"\"\n",
|
||
" Extracts text from a specific PDF page in proper reading order.\n",
|
||
" Adds '\\n' when blocks are vertically separated more than line_tolerance.\n",
|
||
" Removes bullet-like characters (, •, ▪, etc.).\n",
|
||
" \"\"\"\n",
|
||
" doc = fitz.open(pdf_path)\n",
|
||
"\n",
|
||
" if page_num < 1 or page_num > len(doc):\n",
|
||
" return \"\"\n",
|
||
"\n",
|
||
" page = doc[page_num - 1]\n",
|
||
" blocks = page.get_text(\"blocks\") # (x0, y0, x1, y1, text, block_no, block_type)\n",
|
||
"\n",
|
||
" # Sort blocks: top-to-bottom, left-to-right\n",
|
||
" blocks_sorted = sorted(blocks, key=lambda b: (b[1], b[0]))\n",
|
||
"\n",
|
||
" text_lines = []\n",
|
||
" last_y = None\n",
|
||
"\n",
|
||
" for b in blocks_sorted:\n",
|
||
" y0 = b[1]\n",
|
||
" text_block = b[4].strip()\n",
|
||
"\n",
|
||
" # Remove bullet-like characters\n",
|
||
" text_block = re.sub(r\"[•▪◦●❖▶■]\", \"\", text_block)\n",
|
||
"\n",
|
||
" # If new line (based on vertical gap)\n",
|
||
" if last_y is not None and abs(y0 - last_y) > line_tolerance:\n",
|
||
" text_lines.append(\"\") # blank line for spacing\n",
|
||
"\n",
|
||
" text_lines.append(text_block.strip())\n",
|
||
" last_y = y0\n",
|
||
"\n",
|
||
" # Join all lines with real newlines\n",
|
||
" text = \" \".join(text_lines)\n",
|
||
" # Normalize spaces\n",
|
||
" text = re.sub(r\"\\s*\\n\\s*\", \"\\n\", text).strip() # remove spaces around newlines\n",
|
||
" text = re.sub(r\" +\", \" \", text).strip() # collapse multiple spaces to one\n",
|
||
" text = re.sub(r\"\\n{3,}\", \"\\n\\n\", text).strip() # avoid triple blank lines\n",
|
||
" text = text.replace(\"\\n\", \" \").strip()\n",
|
||
" doc.close()\n",
|
||
" return text\n",
|
||
"\n",
|
||
"def evaluate_text(reference, prediction):\n",
|
||
" return {'WER': wer(reference, prediction), 'CER': cer(reference, prediction)}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a79cc4bf",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 3 Model wrapers"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "e30d5b3f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def ocr_easyocr(img, image_array):\n",
|
||
" result = easyocr_reader.readtext(image_array)\n",
|
||
" res = ' '.join([r[1] for r in result]) \n",
|
||
" return res"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"id": "c56f3de2",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import re\n",
|
||
"\n",
|
||
"def _normalize_box_xyxy(box):\n",
|
||
" \"\"\"\n",
|
||
" Accepts:\n",
|
||
" - [[x,y],[x,y],[x,y],[x,y]] (quad)\n",
|
||
" - [x0, y0, x1, y1] (flat)\n",
|
||
" - [x0, y0, x1, y1, x2, y2, x3, y3] (flat quad)\n",
|
||
" Returns (x0, y0, x1, y1)\n",
|
||
" \"\"\"\n",
|
||
" # Quad as list of points?\n",
|
||
" if isinstance(box, (list, tuple)) and box and isinstance(box[0], (list, tuple)):\n",
|
||
" xs = [p[0] for p in box]\n",
|
||
" ys = [p[1] for p in box]\n",
|
||
" return min(xs), min(ys), max(xs), max(ys)\n",
|
||
"\n",
|
||
" # Flat list\n",
|
||
" if isinstance(box, (list, tuple)):\n",
|
||
" if len(box) == 4:\n",
|
||
" x0, y0, x1, y1 = box\n",
|
||
" # ensure order\n",
|
||
" return min(x0, x1), min(y0, y1), max(x0, x1), max(y0, y1)\n",
|
||
" if len(box) == 8:\n",
|
||
" xs = box[0::2]\n",
|
||
" ys = box[1::2]\n",
|
||
" return min(xs), min(ys), max(xs), max(ys)\n",
|
||
"\n",
|
||
" # Fallback\n",
|
||
" raise ValueError(f\"Unrecognized box format: {box!r}\")\n",
|
||
"\n",
|
||
"def ocr_paddle(img, image_array, min_score=0.0, line_tol_factor=0.6):\n",
|
||
" \"\"\"\n",
|
||
" Robust line grouping for PaddleOCR outputs:\n",
|
||
" - normalizes boxes to (x0,y0,x1,y1)\n",
|
||
" - adaptive line tolerance based on median box height\n",
|
||
" - optional confidence filter\n",
|
||
" - inserts '\\n' between lines and preserves left→right order\n",
|
||
" \"\"\"\n",
|
||
" result = paddleocr_model.predict(image_array)\n",
|
||
"\n",
|
||
" boxes_all = [] # (x0, y0, x1, y1, y_mid, text, score)\n",
|
||
" for item in result:\n",
|
||
" res = item.json.get(\"res\", {})\n",
|
||
" boxes = res.get(\"rec_boxes\", []) or [] # be defensive\n",
|
||
" texts = res.get(\"rec_texts\", []) or []\n",
|
||
" scores = res.get(\"rec_scores\", None)\n",
|
||
"\n",
|
||
" for i, (box, text) in enumerate(zip(boxes, texts)):\n",
|
||
" try:\n",
|
||
" x0, y0, x1, y1 = _normalize_box_xyxy(box)\n",
|
||
" except Exception:\n",
|
||
" # Skip weird boxes gracefully\n",
|
||
" continue\n",
|
||
"\n",
|
||
" y_mid = 0.5 * (y0 + y1)\n",
|
||
" score = float(scores[i]) if (scores is not None and i < len(scores)) else 1.0\n",
|
||
"\n",
|
||
" t = re.sub(r\"\\s+\", \" \", str(text)).strip()\n",
|
||
" if not t:\n",
|
||
" continue\n",
|
||
"\n",
|
||
" boxes_all.append((x0, y0, x1, y1, y_mid, t, score))\n",
|
||
"\n",
|
||
" if min_score > 0:\n",
|
||
" boxes_all = [b for b in boxes_all if b[6] >= min_score]\n",
|
||
"\n",
|
||
" if not boxes_all:\n",
|
||
" return \"\"\n",
|
||
"\n",
|
||
" # Adaptive line tolerance\n",
|
||
" heights = [b[3] - b[1] for b in boxes_all]\n",
|
||
" median_h = float(np.median(heights)) if heights else 20.0\n",
|
||
" line_tol = max(8.0, line_tol_factor * median_h)\n",
|
||
"\n",
|
||
" # Sort by vertical mid, then x0\n",
|
||
" boxes_all.sort(key=lambda b: (b[4], b[0]))\n",
|
||
"\n",
|
||
" # Group into lines\n",
|
||
" lines, cur, last_y = [], [], None\n",
|
||
" for x0, y0, x1, y1, y_mid, text, score in boxes_all:\n",
|
||
" if last_y is None or abs(y_mid - last_y) <= line_tol:\n",
|
||
" cur.append((x0, text))\n",
|
||
" else:\n",
|
||
" cur.sort(key=lambda t: t[0])\n",
|
||
" lines.append(\" \".join(t[1] for t in cur))\n",
|
||
" cur = [(x0, text)]\n",
|
||
" last_y = y_mid\n",
|
||
"\n",
|
||
" if cur:\n",
|
||
" cur.sort(key=lambda t: t[0])\n",
|
||
" lines.append(\" \".join(t[1] for t in cur))\n",
|
||
"\n",
|
||
" res = \" \".join(lines)\n",
|
||
" res = re.sub(r\"\\s+\\n\", \"\\n\", res).strip()\n",
|
||
" return res\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"id": "4273c645",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def doctr_blocks_to_text(doctr_result):\n",
|
||
" \"\"\"\n",
|
||
" Convierte la salida estructurada de DocTR en texto plano preservando el orden de lectura.\n",
|
||
" Estructura: Document -> pages -> blocks -> lines -> words\n",
|
||
" \"\"\"\n",
|
||
" out_lines = []\n",
|
||
" for page in doctr_result.pages:\n",
|
||
" for block in page.blocks:\n",
|
||
" for line in block.lines:\n",
|
||
" out_lines.append(\" \".join([w.value for w in line.words]))\n",
|
||
" out_lines.append(\"\") # separador de párrafos\n",
|
||
" return \" \".join([l for l in out_lines if l is not None]).strip()\n",
|
||
"\n",
|
||
"def ocr_doctr(img, image_array):\n",
|
||
" doc = [image_array]\n",
|
||
" result = doctr_model(doc) # inferencia\n",
|
||
" text = doctr_blocks_to_text(result) \n",
|
||
" return text"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e42cae29",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4 Run AI OCR Benchmark"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"id": "9b55c154",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref:\n",
|
||
"Instrucciones para la redacción y elaboración del TFE Máster Universitario en Inteligencia Artificial\n",
|
||
"easy_text:\n",
|
||
"Instrucciones para la redacción y elaboración del TFE Máster Universitario en Inteligencia Artificial\n",
|
||
"docTR_text:\n",
|
||
"Instrucciones para la redaccion y elaboracion del TFE Master Universitario en Inteligencia Artificial\n",
|
||
"paddle_text:\n",
|
||
"Instrucciones para la redacción y elaboración del TFE Máster Universitario en Inteligencia Artificial\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
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"ref:\n",
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"Índice 1. Indicaciones generales 3 1.1. Línea de discurso 3 1.2. Estructura general y extensión del TFE 4 1.3. Formatos y plantilla de trabajo 5 1.4. Estética y estilo de redacción 7 1.5. Normativa de citas 8 2. Estructura del documento 9 2.1. Resumen 10 2.2. Organización del trabajo en grupo 11 2.3. Introducción 11 2.4. Contexto y estado del arte 13 © Universidad Internacional de La Rioja (UNIR) 2.5. Objetivos concretos y metodología de trabajo 14 2.6. Desarrollo específico de la contribución 17 2.7. Conclusiones y trabajo futuro 20 2.8. Referencias bibliográficas 21 2.8.1. Herramientas para buscar bibliografía 22 2.9. Anexos 23 2.10. Índice de acrónimos 24\n",
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"Indice 1. Indicaciones generales 3 1.1. Línea de discurso 3 1.2. Estructura general y extensión del TFE 1.3. Formatos y plantilla de trabajo ; 1.4. Estética y estilo de redacción 1.5. Normativa de citas 8 2. Estructura del documento 9 2.1. Resumen 10 2.2. Organización del trabajo en grupo 11 2.3. Introducción 11 2.4. Contexto y estado del arte 13 2 2.5. Objetivos concretos y metodología de trabajo 14 ; 2.6. Desarrollo específico de la contribución 17 9 2.7. Conclusiones y trabajo futuro 20 g 2.8. Referencias bibliográficas 21 1 2.8.1 Herramientas para buscar bibliografía 22 2.9. Anexos 23 1 2.10 Indice de acrónimos 24 0\n",
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"docTR_text:\n",
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"Indice 1. Indicaciones generales 3 1.1. Linea de discurso 3 1.2. Estructura general y extension del TFE 4 1.3. Formatos y plantilla de trabajo 5 1.4. Estética y estilo de redaccion 7 1.5. Normativa de citas 8 2. Estructura del documento 9 2.1. Resumen 10 2.2. Organizacion del trabajo en grupo 11 2.3. Introduccion 11 2.4. Contexto y estado del arte 13 = 2.5. Objetivos concretos y metodologia de trabajo 14 2.6. Desarrollo especifico de la contribucion 17 2.7. Conclusiones y trabajo futuro 20 2.8. Referencias bibliograficas 21 2.8.1. Herramientas para buscar bibliografia 22 2.9. Anexos 23 2.10. Indice de acronimos 24\n",
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"paddle_text:\n",
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"índice 1. Indicaciones generales 3 1.1. Línea de discurso 3 1.2. Estructura general y extensión del TfE 4 1.3. Formatos y plantilla de trabajo 5 1.4. Estética y estilo de redacción 7 1.5. Normativa de citas 8 2. Estructura del documento 9 2.1. Resumen 10 2.2. Organización del trabajo en grupo 11 2.3. Introducción 11 2.4. Contexto y estado del arte 13 2.5. Objetivos concretos y metodologia de trabajo 14 2.6. Desarrollo especifico de la contribucion 17 2.7. Conclusiones y trabajo futuro 20 © Universidad Internacional de La Rioja (UNiR) 2.8. Referencias bibliográficas 21 2.8.1. Herramientas para buscar bibliografía 22 2.9. Anexos 23 2.10. índice de acrónimos 24\n"
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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref:\n",
|
||
"1. Indicaciones generales Este documento ofrece indicaciones para la elaboración de tu TFE. Como cualquier documento académico sigue una estructura bastante formal y una línea de discurso que debe facilitar su lectura y reflejar el trabajo que has venido realizando durante estos meses, correspondiente con una carga de 12 ECTS. Para su preparación dispones en el aula virtual de una plantilla individual si se realiza el TFE individualmente o de una plantilla grupal si se realiza el TFE en grupo, que deberás descargar y utilizar desde el primer momento. Es la base para las entregas de todos los borradores, respetando su estructura general y los estilos predefinidos que también se explican en este documento. Un TFE es una obra literaria de carácter académico. Recoge algún tipo de contribución en un ámbito del saber que demuestra los conocimientos adquiridos en la materia. Es importante por los siguientes motivos: Su estructura demuestra tu capacidad para presentar el contenido de manera organizada, ajustándote a las directrices de la titulación y facilitando su lectura. Su contenido recoge los resultados de tu trabajo, y refleja la adquisición de competencias en el resto de las asignaturas de la titulación. Es el documento final que calificarán, empleando la rúbrica de evaluación, tu director y el profesor evaluador (en el caso del TFG) o la comisión evaluadora (en el caso de TFM). 1.1. Línea de discurso © Universidad Internacional de La Rioja (UNIR) La línea de discurso parte en general de una necesidad o un problema claramente identificado. A partir de ese problema se revisa la literatura y otras evidencias disponibles con un doble objetivo: Instrucciones para la redacción y elaboración del TFE 3 Máster Universitario en Inteligencia Artificial\n",
|
||
"easy_text:\n",
|
||
"1. Indicaciones generales Este documento ofrece indicaciones para la elaboración de tu TFE. Como cualquier documento académico sigue una estructura bastante formal y una línea de discurso que debe facilitar su lectura y reflejar el trabajo que has venido realizando durante estos meses , correspondiente con una carga de 12 ECTS. Para su preparación dispones en el aula virtual de una plantilla individual si se realiza el TFE individualmente 0 de una plantilla grupal si se realiza el TFE en grupo, que deberás descargar y utilizar desde el primer momento. Es la base para las entregas de todos los borradores, respetando su estructura general y los estilos predefinidos que también se explican en este documento. Un TFE es una obra literaria de carácter académico. Recoge algún tipo de contribución en un ámbito del saber que demuestra los conocimientos adquiridos en la materia. Es importante por los siguientes motivos: Su estructura demuestra tu capacidad para presentar el contenido de manera organizada; ajustándote a las directrices de la titulación y facilitando su lectura. Su contenido recoge los resultados de tu trabajo, Y refleja la adquisición de competencias en el resto de las asignaturas de la titulación. Es el documento final que calificarán, empleando la rúbrica de evaluación, tu director Y el profesor evaluador (en el caso del TFG) 0 la comisión evaluadora (en el caso de TFM) 2 1.1. Línea de discurso ; 9 g La línea de discurso parte en general de una necesidad 0 un problema claramente 1 identificado. A de ese problema se revisa la literatura Y otras evidencias disponibles con un doble objetivo: 1 Instrucciones para la redacción y elaboración del TFE 3 0 Máster Universitario en Inteligencia Artificial partir\n",
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"docTR_text:\n",
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"1. Indicaciones generales Este documento ofrece indicaciones para la elaboracion de tu TFE. Como cualquier documento académico sigue una estructura bastante formal y una linea de discurso que debe facilitar su lectura y reflejar el trabajo que has venido realizando durante estos meses, correspondiente con una carga de 12 ECTS. Para su preparacion dispones en el aula virtual de una plantilla individual si se realiza el TFE individualmente O de una plantilla grupal si se realiza el TFE en grupo, que deberas descargar y utilizar desde el primer momento. Es la base para las entregas de todos los borradores, respetando su estructura general y los estilos predefinidos que también se explican en este documento. Un TFE es una obra literaria de caracter académico. Recoge algun tipo de contribucion en un ambito del saber que demuestra los conocimientos adquiridos en la materia. Es importante por los siguientes motivos: Su estructura demuestra tu capacidad para presentar el contenido de manera organizada, ajustandote a las directrices de la titulacion y facilitando su lectura. Su contenido recoge los resultados de tu trabajo, y refleja la adquisicion de competencias en el resto de las asignaturas de la titulacion. Es el documento final que calificaran, empleando la rubrica de evaluacion, tu director y el profesor evaluador (en el caso del TFG) O la comision evaluadora (en el caso de TFM). 1.1. Linea de discurso La linea de discurso parte en general de una necesidad O un problema claramente identificado. A partir de ese problema se revisa la literatura y otras evidencias disponibles con un doble objetivo: Instrucciones para la redaccion y elaboracion del TFE 3 Master Universitario en Inteligencia Artificial\n",
|
||
"paddle_text:\n",
|
||
"1. Indicaciones generales Este documento ofrece indicaciones para la elaboración de tu TFe. Como cualquier documento académico sigue una estructura bastante formal y una línea de discurso que debe facilitar su lectura y reflejar el trabajo que has venido realizando durante estos meses, correspondiente con una carga de 12 ECTS. Para su preparación dispones en el aula virtual de una plantilla individual si se realiza el TFE individualmente o de una plantilla grupal si se realiza el TFE en grupo, que deberás descargar y utilizar desde el primer momento. Es la base para las entregas de todos los borradores, respetando su estructura general y los estilos predefinidos que también se explican en este documento. Un TFE es una obra literaria de carácter académico. Recoge algún tipo de contribución en un ámbito del saber que demuestra los conocimientos adquiridos en la materia. Es importante por los siguientes motivos: Su estructura demuestra tu capacidad para presentar el contenido de manera organizada, ajustándote a las directrices de la titulación y facilitando su lectura. Su contenido recoge los resultados de tu trabajo,y refleja la adquisición de competencias en el resto de las asignaturas de la titulación. Es el documento final que calificarán, empleando la rúbrica de evaluación, tu director y el profesor evaluador (en el caso del TfG) o la comisión evaluadora (en el caso de TFM). 1.1. Linea de discurso La línea de discurso parte en general de una necesidad o un problema claramente © Universidad Internacional de La Rioja (UNiR) identificado. A partir de ese problema se revisa la literatura y otras evidencias disponibles con un doble objetivo: Instrucciones para la redacción y elaboración del TFE 3 Máster Universitario en Inteligencia Artificial\n"
|
||
]
|
||
},
|
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{
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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref:\n",
|
||
"1. Analizar el problema o necesidad profundizando en su comprensión. Se trata de demostrar su relevancia y un cierto nivel de conocimiento sobre el mismo. 2. Conocer diferentes alternativas para solucionar esa necesidad, demostrando que entiendes de qué manera han abordado otras personas este problema u otros similares. Sobre este análisis de la literatura, informes, datos estadísticos o estudios de mercado, dependiendo de la temática del TFE, podrás fundamentar sólidamente la contribución de tu trabajo. Esta contribución se apoya generalmente en una serie de objetivos o hipótesis de investigación. Finalmente, el trabajo debería presentar algún tipo de evaluación o justificación de viabilidad o validez de la solución propuesta. 1.2. Estructura general y extensión del TFE El TFE sigue una estructura general fija, definida para reflejar lo explicado en el apartado anterior y homogeneizar los diferentes trabajos facilitando su lectura e interpretación. Los principales apartados son los siguientes: Portada. Respeta el formato y estructura definidos en la plantilla. Resumen breve / abstract: al principio del trabajo debes incluir un breve resumen de 150 palabras. Indicarás además de 3 a 5 descriptores (palabras clave o keywords) que permitan catalogar el trabajo adecuadamente. Índices numerados: Separando “Índice de contenidos”, “Índice de figuras” e “Índice de tablas”, según proceda. Cada índice debe comenzar en una nueva página del documento. © Universidad Internacional de La Rioja (UNIR) Organización del trabajo en grupo: Únicamente en el caso de TFE grupal. Desarrollo en capítulos: el contenido del TFE se estructura en capítulos numerados que comienzan en una nueva página. Instrucciones para la redacción y elaboración del TFE 4 Máster Universitario en Inteligencia Artificial\n",
|
||
"easy_text:\n",
|
||
"1 Analizar el problema 0 necesidad profundizando en su comprensión. Se trata de demostrar su relevancia y un cierto nivel de conocimiento sobre el mismo. 2. Conocer diferentes alternativas para solucionar esa necesidad, demostrando que entiendes de manera han abordado otras personas este problema u otros similares. Sobre este análisis de la literatura, informes, datos estadísticos 0 estudios de mercado; dependiendo de la temática del TFE, podrás fundamentar sólidamente la contribución de tu trabajo. Esta contribución se apoya generalmente en una serie de objetivos 0 hipótesis de investigación. Finalmente, el trabajo debería presentar algún tipo de evaluación 0 justificación de viabilidad 0 validez de la solución propuesta. 1.2. Estructura general y extensión del TFE El TFE sigue una estructura general fija, definida para reflejar lo explicado en el apartado anterior Y homogeneizar los diferentes trabajos facilitando su lectura e interpretación. Los principales apartados son los siguientes: Portada. Respeta el formato y estructura definidos en la plantilla . Resumen breve abstract: al principio del trabajo debes incluir un breve resumen de 150 palabras. Indicarás además de 3 a 5 descriptores (palabras clave 0 keywords) que permitan catalogar el trabajo adecuadamente. Indices numerados: Separando \"Indice de contenidos\" \"Indice de figuras\" e \"Indice de tablas\" proceda. Cada índice debe comenzar en una nueva página del documento. 2 Organización del trabajo en grupo: Únicamente en el caso de TFE grupal. ; Desarrollo en capítulos: el contenido del TFE se estructura en capítulos 9 g numerados que comienzan en una nueva página. 1 1 Instrucciones para la redacción y elaboración del TFE 0 Máster Universitario en Inteligencia Artificial qué según\n",
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"1. Analizar el problema O necesidad profundizando en su comprension. Se trata de demostrar su relevancia y un cierto nivel de conocimiento sobre el mismo. 2. Conocer diferentes alternativas para solucionar esa necesidad, demostrando que entiendes de qué manera han abordado otras personas este problema u otros similares. Sobre este analisis de la literatura, informes, datos estadisticos O estudios de mercado, dependiendo de la tematica del TFE, podras fundamentar solidamente la contribucion de tu trabajo. Esta contribucion se apoya generalmente en una serie de objetivos O hipotesis de investigacion. Finalmente, el trabajo deberia presentar algun tipo de evaluacion O justificacion de viabilidad O validez de la solucion propuesta. 1.2. Estructura general y extension del TFE El TFE sigue una estructura general fija, definida para reflejar lo explicado en el apartado anterior y homogeneizar los diferentes trabajos facilitando su lectura e interpretacion. Los principales apartados son los siguientes: Portada. Respeta el formato y estructura definidos en la plantilla. Resumen breve / abstract: al principio del trabajo debes incluir un breve resumen de 150 palabras. Indicaras ademas de 3 a 5 descriptores (palabras clave O keywords) que permitan catalogar el trabajo adecuadamente. N indices numerados: Separando \"indice de contenidos\", \"indice de figuras\" e \"Indice de tablas\", segun proceda. Cada indice debe comenzar en una nueva pagina del documento. Organizacion del trabajo en grupo: Unicamente en el caso de TFE grupal. Desarrollo en capitulos: el contenido del TFE se estructura en capitulos numerados que comienzan en una nueva pagina. Instrucciones para la redaccion y elaboracion del TFE 4 Master Universitario en Inteligencia Artificial\n",
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"paddle_text:\n",
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"1. Analizar el problema o necesidad profundizando en su comprensión. Se trata de demostrar su relevancia y un cierto nivel de conocimiento sobre el mismo. 2. Conocer diferentes alternativas para solucionar esa necesidad, demostrando que entiendes de qué manera han abordado otras personas este problema u otros similares. Sobre este analisis de la literatura, informes,datos estadisticos o estudios de mercado, dependiendo de la temática del TfE, podrás fundamentar solidamente la contribución de tu trabajo. Esta contribución se apoya generalmente en una serie de objetivos o hipótesis de investigación. Finalmente, el trabajo debería presentar algún tipo de evaluación o justificación de viabilidad o validez de la solución propuesta. 1.2.E Estructura general y extensión del tFE El TFE sigue una estructura general fija, definida para reflejar lo explicado en el apartado anterior y homogeneizar los diferentes trabajos facilitando su lectura e interpretación. Los principales apartados son los siguientes: Portada. Respeta el formato y estructura definidos en la plantilla. Resumen breve / abstract: al principio del trabajo debes incluir un breve resumen de 150 palabras. Indicarás además de 3 a 5 descriptores (palabras clave o keywords) que permitan catalogar el trabajo adecuadamente. indices numerados: Separando \"índice de contenidos\",\"índice de figuras\"e \"índice de tablas\", según proceda. Cada indice debe comenzar en una nueva página del documento. Organización del trabajo en grupo: Unicamente en el caso de TFE grupal. Desarrollo en capítulos: el contenido del TFE se estructura en capítulos numerados que comienzan en una nueva página. © Universidad Internacional de La Rioja (UNiR) Instrucciones para la redacción y elaboración del Tfe Máster Universitario en Inteligencia Artificial 4\n"
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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref:\n",
|
||
"Referencias bibliográficas: el trabajo debe incluir una sección de bibliografía, en la que aparezcan en formato APA los detalles de todos los documentos a los que se haga referencia en el TFE. Anexos: estos son apartados opcionales que contienen cuestionarios, encuestas, resultados de pilotos, documentos adicionales, capturas de pantalla, y otros elementos que complementan o amplían la información del trabajo. Los anexos se diferencian empleando una letra (Anexo A, Anexo B…). En el punto 2 se describen con mayor detalle cada uno de los apartados del TFE. La extensión mínima en un TFE individual es de 50 páginas y la máxima de 90 páginas, sin contar portada, índices y anexos. En el caso de un TFE grupal, la extensión del TFE debe garantizar que cada uno de los integrantes del equipo ha dedicado a la elaboración y defensa del trabajo las competencias previstas en la memoria. A modo orientativo, y para garantizar la calidad y dedicación que requiere un TFE grupal, la extensión mínima ha de ser un 50% superior a lo previsto para un TFE individual. Por lo tanto, la extensión mínima en un TFE grupal es de 75 páginas. 1.3. Formatos y plantilla de trabajo Para la elaboración del TFE, ya sea individual o grupal, debes utilizar la plantilla disponible en el aula virtual. No modifiques los estilos definidos en esta plantilla: márgenes, interlineado, tipos de letra, etc. Para cualquier duda que pueda surgirte, puedes consultar esos formatos a continuación. © Universidad Internacional de La Rioja (UNIR) El trabajo deberá estar escrito en tamaño de página A4 con los siguientes márgenes: Izquierdo: 3,0 cm. Derecho: 2,0 cm. Instrucciones para la redacción y elaboración del TFE 5 Máster Universitario en Inteligencia Artificial\n",
|
||
"easy_text:\n",
|
||
"Referencias bibliográficas: el trabajo debe incluir una sección de bibliografía, en la que aparezcan en formato APA los detalles de todos los documentos a los que se haga referencia en el TFE. Anexos: estos son apartados opcionales que contienen cuestionarios, encuestas, resultados de pilotos, documentos adicionales, capturas de pantalla; Y otros elementos que complementan 0 amplían la información del trabajo. Los anexos se diferencian empleando una letra (Anexo A, Anexo B.). En el punto 2 se describen con mayor detalle cada uno de los apartados del TFE La extensión mínima en un TFE individual es de 50 páginas y la máxima de 90 páginas, sin contar portada, índices y anexos. En el caso de un TFE grupal, la extensión del TFE debe garantizar que cada uno de los integrantes del equipo ha dedicado a la elaboración Y defensa del trabajo las competencias previstas en la memoria. A modo orientativo; Y para garantizar la calidad y dedicación que requiere un TFE grupal; la extensión mínima ha de ser un 50% superior a lo previsto para un TFE individual. Por lo tanto; la extensión mínima en un TFE grupal es de 75 páginas. 1.3. Formatos y plantilla de trabajo Para la elaboración del TFE, ya sea individual 0 grupal, debes utilizar la plantilla disponible en el aula virtual. No modifiques los estilos definidos en esta plantilla: márgenes, interlineado; tipos de letra, etc. Para cualquier duda que pueda surgirte, puedes consultar esos formatos a continuación. 2 ; 9 El trabajo deberá estar escrito en tamaño de página A4 con los siguientes g márgenes: 1 Izquierdo: 3,0 cm. Derecho: 2,0 cm 1 Instrucciones para la redacción y elaboración del TFE 5 0 Máster Universitario en Inteligencia Artificial\n",
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"docTR_text:\n",
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"Referencias bibliograficas: el trabajo debe incluir una seccion de bibliografia, en la que aparezcan en formato APA los detalles de todos los documentos a los que se haga referencia en el TFE. Anexos: estos son apartados opcionales que contienen cuestionarios, encuestas, resultados de pilotos, documentos adicionales, capturas de pantalla, y otros elementos que complementan O amplian la informacion del trabajo. Los anexos se diferencian empleando una letra (Anexo A, Anexo B...). En el punto 2 se describen con mayor detalle cada uno de los apartados del TFE. La extension minima en un TFE individual es de 50 paginas y la maxima de 90 paginas, sin contar portada, indices y anexos. En el caso de un TFE grupal, la extension del TFE debe garantizar que cada uno de los integrantes del equipo ha dedicado a la elaboracion y defensa del trabajo las competencias previstas en la memoria. A modo orientativo, y para garantizar la calidad y dedicacion que requiere un TFE grupal, la extension minima ha de ser un 50% superior a lo previsto para un TFE individual. Por lo tanto, la extension minima en un TFE grupal es de 75 paginas. 1.3. Formatos y plantilla de trabajo Para la elaboracion del TFE, ya sea individual O grupal, debes utilizar la plantilla disponible en el aula virtual. No modifiques los estilos definidos en esta plantilla: margenes, interlineado, tipos de letra, etc. Para cualquier duda que pueda surgirte, puedes consultar esos formatos a continuacion. El trabajo debera estar escrito en tamano de pagina A4 con los siguientes margenes: Izquierdo: 3,0 cm. Derecho: 2,0 cm. Instrucciones para la redaccion y elaboracion del TFE 5 Master Universitario en Inteligencia Artificial\n",
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||
"paddle_text:\n",
|
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"Referencias bibliográficas: el trabajo debe incluir una sección de bibliografía, en la que aparezcan en formato APA los detalles de todos los documentos a los que se haga referencia en el TFE. Anexos: estos son apartados opcionales que contienen cuestionarios, encuestas, resultados de pilotos, documentos adicionales, capturas de pantalla,y otros elementos que complementan o amplían la información del trabajo. Los anexos se diferencian empleando una letra (Anexo A, Anexo B.….). En el punto 2 se describen con mayor detalle cada uno de los apartados del TfE. La extensión mínima en un TfE individual es de 50 páginas y la máxima de 90 páginas, sin contar portada,índices y anexos. En el caso de e un TFE grupal, la extensión del TFE debe garantizar que cada uno de los integrantes del equipo ha dedicado a I la elaboración y defensa del trabajo las competencias previstas en la memoria. A modo orientativo,y para garantizar la calidad y dedicación que requiere un TFE grupal, la extensión mínima ha de ser un 50% superior a lo previsto para un TFE individual. Por lo tanto, la extensión mínima en un TFE grupal es de 75 páginas. 1.3. Formatos y plantilla de trabajo Para la elaboración del TFE,ya sea individual o grupal, debes utilizar la plantilla disponible en el aula virtual. No modifiques los estilos definidos en esta plantilla: margenes, interlineado, tipos de letra, etc. Para cualquier duda que pueda surgirte, puedes consultar esos formatos a continuacion. El trabajo deberá estar escrito en tamaño de página A4 con los siguientes márgenes: © Universidad Internacional de La Rioja (UNiR) Izquierdo: 3,0 cm. Derecho: 2,0 cm. Instrucciones para la redacción y elaboración del TfE 5 Máster Universitario en Inteligencia Artificial\n"
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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref:\n",
|
||
"Superior e inferior: 2,5 cm. Formato de párrafo en texto principal (estilo de la plantilla “Normal”): Calibri 12, justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos posterior, sin sangría. Títulos: Primer nivel (estilo de la plantilla “Título 1”): Calibri Light 18, azul, justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos posterior, sin sangría. Segundo nivel (estilo de la plantilla “Título 2”): Calibri Light 14, azul, justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos posterior, sin sangría. Tercer nivel (estilo de la plantilla “Título 3”: Calibri Light 12, justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos posterior, sin sangría. Notas al pie: Calibri 10, justificado, interlineado sencillo, espacio entre párrafos 0 puntos anterior y 0 puntos posterior, sin sangría. Tablas y figuras: Título en la parte superior de la tabla o figura. Numeración tabla o figura (Tabla 1/ Figura1): Calibri 12, negrita, justificado. Nombre tabla o figura: Calibri 12, cursiva, justificado. Cuerpo: la tipografía de las tablas o figuras se pueden reducir hasta los 9 puntos si estas contienen mucha información. Si la tabla o figura es muy grande, también se puede colocar en apaisado dentro de la hoja. Fuente de la tabla o figura en la parte inferior. Calibri 9,5, centrado. © Universidad Internacional de La Rioja (UNIR) Encabezado y pie de página: Todas las páginas llevarán un encabezado con el nombre completo del estudiante y el título del TFE. Todas las páginas llevarán también un pie de página con el número de página. Instrucciones para la redacción y elaboración del TFE 6 Máster Universitario en Inteligencia Artificial\n",
|
||
"easy_text:\n",
|
||
"Superior e inferior: 2,5 cm_ Formato de párrafo en texto principal (estilo de la plantilla \"Normal\"): Calibri 12, justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos posterior, sin sangría. Títulos: Primer nivel (estilo de la plantilla \"Título 1\"): Calibri Light 18, azul, justificado; interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos posterior, sin sangría. Segundo nivel (estilo de la plantilla \"Título 2\"): Calibri Light 14, azul, justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos posterior, sin sangría. Tercer nivel (estilo de la plantilla \"Título 3\" = Calibri Light 12, justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos posterior, sin sangría. Notas al pie: Calibri 10, justificado; interlineado sencillo; espacio entre párrafos 0 puntos anterior y 0 puntos posterior, sin sangría. Tablas y figuras: Título en la parte superior de la tabla 0 figura. Numeración tabla 0 figura (Tabla 1/ Figural): Calibri negrita; justificado. Nombre tabla 0 figura: Calibri 12, cursiva, justificado. Cuerpo: la tipografía de las tablas 0 figuras se pueden reducir hasta los 9 puntos si estas contienen mucha información. Si la tabla 0 figura es muy grande, también se puede colocar en apaisado dentro de la hoja. Fuente de la tabla 0 figura en la parte inferior. Calibri 9,5, centrado. 2 ; Encabezado y pie de página: 9 Todas las páginas Ilevarán un encabezado con el nombre completo del g estudiante y el título del TFE. 1 Todas las páginas llevarán también un pie de página con el número de página. 1 Instrucciones para la redacción y elaboración del TFE 0 Máster Universitario en Inteligencia Artificial 12,\n",
|
||
"docTR_text:\n",
|
||
"Superior e inferior: 2,5 cm. Formato de parrafo en texto principal (estilo de la plantilla \"Normal\"): Calibri 12, justificado, interlineado 1,5, espacio entre parrafos 6 puntos anterior y 6 puntos posterior, sin sangria. Titulos: Primer nivel (estilo de la plantilla \"Titulo 1\"): Calibri Light 18, azul, justificado, interlineado 1,5, espacio entre parrafos 6 puntos anterior y 6 puntos posterior, sin sangria. Segundo nivel (estilo de la plantilla \"Titulo 2\"): Calibri Light 14, azul, justificado, interlineado 1,5, espacio entre parrafos 6 puntos anterior y 6 puntos posterior, sin sangria. Tercer nivel (estilo de la plantilla \"Titulo 3\": Calibri Light 12, justificado, interlineado 1,5, espacio entre parrafos 6 puntos anterior y 6 puntos posterior, sin sangria. Notas al pie: Calibri 10, justificado, interlineado sencillo, espacio entre parrafos 0 puntos anterior y 0 puntos posterior, sin sangria. Tablas y figuras: Titulo en la parte superior de la tabla O figura. Numeracion tabla O figura (Tabla 1/ Figural): Calibri 12, negrita, justificado. Nombre tabla O figura: Calibri 12, cursiva, justificado. Cuerpo: la tipografia de las tablas O figuras se pueden reducir hasta los 9 puntos si estas contienen mucha informacion. Si la tabla O figura es muy grande, también se puede colocar en apaisado dentro de la hoja. Fuente de la tabla O figura en la parte inferior. Calibri 9,5, centrado. Encabezado y pie de pagina: Todas las paginas llevaran un encabezado con el nombre completo del estudiante y el titulo del TFE. Todas las paginas llevaran también un pie de pagina con el numero de pagina. Instrucciones para la redaccion y elaboracion del TFE 6 Master Universitario en Inteligencia Artificial\n",
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"paddle_text:\n",
|
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"Superior e inferior: 2,5 cm. Formato de párrafo en texto principal (estilo de la plantilla “Normal\"): Calibri 12, justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos posterior, sin sangría. Títulos: Primer nivel (estilo de la plantillaTítulo 1\"): Calibri Light 18, azul, justificado, interlineado 1,5,espacio entre párrafos 6 puntos anterior y 6 puntos posterior, sin sangría. Segundo nivel (estilo de la plantilla Titulo 2\"): Calibri Light 14, azul, justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos posterior, sin sangría. Tercer nivel (estilo de la plantilla Título 3\": Calibri Light 12, justificado, interlineado 1,5,espacio entre párrafos 6 puntos anterior y 6 puntos posterior, sin sangría. Notas al pie: Calibri 10, justificado, interlineado sencillo, espacio entre párrafos O puntos anterior y O puntos posterior, sin sangra. Tablas y figuras: Título en la parte superior de la tabla o figura. Numeración tabla o figura (Tabla 1/ Figura1): Calibri 12, negrita, justificado. Nombre tabla o figura: Calibri 12, cursiva, justificado. Cuerpo: la tipografía de las tablas o figuras se pueden reducir hasta los 9 puntos si estas contienen mucha información. Si la tabla o figura es muy grande, también se puede colocar en apaisado dentro de la hoja. Fuente de la tabla o figura en la parte inferior. Calibri 9,5, centrado. Encabezado y pie de página: Todas las páginas llevarán un encabezado con el nombre completo del estudiante y el título del TFE. © Universidad Internacional de La Rioja (UNiR) Todas las páginas llevarán también un pie de página con el número de página. Instrucciones para la redacción y elaboración del TFE Máster Universitario en Inteligencia Artificial 9\n"
|
||
]
|
||
},
|
||
{
|
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"data": {
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"ref:\n",
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"Los borradores intermedios deberán entregarse en formato Word. El documento final deberá depositarse en formato PDF. 1.4. Estética y estilo de redacción Es fundamental que el TFE presente un aspecto elegante y correcto. Se trata de un trabajo académico y debe reflejar la madurez y el nivel formativo de una persona que ha finalizado un estudio de grado o postgrado. Ten en cuenta las siguientes recomendaciones en todas y cada una de las entregas que realices y, en especial, en el depósito final del documento: Verifica la originalidad del documento, asegurándote de que citas todas las fuentes consultadas y no existen textos de autoría ajena sin referenciar correctamente. Cuida la presentación del trabajo. Comprueba que formatos como tipo y tamaño de letra, número de páginas, encabezados, justificación de párrafos, interlineado, etc., son correctos. Revisa la ortografía y la redacción. Utiliza el corrector de Word para asegurarte de que no has dejado ninguna errata. Una lectura detenida del documento también te ayudará a detectar erratas, omisiones o redundancias. Si es posible, pide a alguien cercano que lo lea y te dé su opinión sobre la redacción. Presta especial atención a los siguientes aspectos: - Que los párrafos sigan un orden o hilo argumental lógico. - Que la información se presente de una manera que facilite su © Universidad Internacional de La Rioja (UNIR) comprensión, definiendo los conceptos necesarios e incluyendo las citas bibliográficas pertinentes. - Elimina párrafos demasiado cortos. Cada párrafo debería tener al menos tres oraciones. - Elimina frases superfluas y repeticiones de ideas. Instrucciones para la redacción y elaboración del TFE 7 Máster Universitario en Inteligencia Artificial\n",
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"easy_text:\n",
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"Los borradores intermedios deberán entregarse en formato Word. El documento final deberá depositarse en formato PDF. 1.4. Estética y estilo de redacción Es fundamental que el TFE presente un aspecto elegante y correcto. Se trata de un trabajo académico y debe reflejar la madurez y el nivel formativo de una persona que ha finalizado un estudio de grado 0 postgrado. Ten en cuenta las siguientes recomendaciones en todas y cada una de las entregas que realices Y, en especial, en el depósito final del documento: Verifica la originalidad del documento, asegurándote de que citas todas las fuentes consultadas y no existen textos de autoría ajena sin referenciar correctamente Cuida la presentación del trabajo. Comprueba que formatos como tipo y tamaño de letra, número de páginas, encabezados, justificación de párrafos, interlineado; etc., son correctos. Revisa la ortografía y la redacción. Utiliza el corrector de Word para asegurarte de que no has dejado ninguna errata. Una lectura detenida del documento también te ayudará a detectar erratas, omisiones 0 redundancias. Si es posible; a alguien cercano que lo lea y te dé su opinión sobre la redacción. Presta especial atención a los siguientes aspectos: Que los párrafos sigan un orden 0 hilo argumental lógico. Que la información se presente de una manera que facilite su 2 comprensión; definiendo los conceptos necesarios e incluyendo las citas ; 9 bibliográficas pertinentes. g Elimina párrafos demasiado cortos. Cada párrafo debería tener al menos 1 tres oraciones. Elimina frases superfluas y repeticiones de ideas. 1 Instrucciones para la redacción y elaboración del TFE 0 Máster Universitario en Inteligencia Artificial pide\n",
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"docTR_text:\n",
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"Los borradores intermedios deberan entregarse en formato Word. El documento final debera depositarse en formato PDF. 1.4. Estética y estilo de redaccion Es fundamental que el TFE presente un aspecto elegante y correcto. Se trata de un trabajo académico y debe reflejar la madurez y el nivel formativo de una persona que ha finalizado un estudio de grado O postgrado. Ten en cuenta las siguientes recomendaciones en todas y cada una de las entregas que realices Y, en especial, en el deposito final del documento: Verifica la originalidad del documento, asegurandote de que citas todas las fuentes consultadas y no existen textos de autoria ajena sin referenciar correctamente. Cuida la presentacion del trabajo. Comprueba que formatos como tipo y tamano de letra, numero de paginas, encabezados, justificacion de parrafos, interlineado, etc., son correctos. Revisa la ortografia y la redaccion. Utiliza el corrector de Word para asegurarte de que no has dejado ninguna errata. Una lectura detenida del documento también te ayudara a detectar erratas, omisiones O redundancias. Si es posible, pide a alguien cercano que lo lea y te dé su opinion sobre la redaccion. Presta especial atencion a los siguientes aspectos: Que los parrafos sigan un orden O hilo argumental logico. Que la informacion se presente de una manera que facilite su comprension, definiendo los conceptos necesarios e incluyendo las citas bibliograficas pertinentes. Elimina parrafos demasiado cortos. Cada parrafo deberia tener al menos tres oraciones. Elimina frases superfluas y repeticiones de ideas. Instrucciones para la redaccion y elaboracion del TFE 7 Master Universitario en Inteligencia Artificial\n",
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"paddle_text:\n",
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"Los borradores intermedios deberán entregarse en formato Word. El documento final deberá depositarse en formato PDf. 1.4. Estética y estilo de redacción Es fundamental que el TFE presente un aspecto elegante y correcto. Se trata de un trabajo académico y debe reflejar la madurez y el nivel formativo de una persona que ha finalizado un estudio de grado o postgrado. Ten en cuenta las siguientes recomendaciones en todas y cada una de las entregas que realices y, en especial, en el deposito final del documento: Verifica la originalidad del documento,asegurándote de que citas todas las fuentes consultadas y no existen textos de autoría ajena sin referenciar correctamente. Cuida la presentación del trabajo. Comprueba que formatos como tipo y tamaño de letra, número de páginas, encabezados, justificación de párrafos, interlineado, etc., son correctos. Revisa la ortografía y la redacción. Utiliza el corrector de Word para asegurarte de que no has dejado ninguna errata. Una lectura detenida del documento también te ayudará a detectar erratas, omisiones o redundancias. Si es posible, pide a alguien cercano que lo lea y te dé su opinión sobre la redacción. Presta especial atención a los siguientes aspectos: Que los párrafos sigan un orden o hilo argumental lógico. Que la información se presente de una manera que facilite su comprensión, definiendo los conceptos necesarios e incluyendo las citas bibliograficas pertinentes. Elimina párrafos demasiado cortos. Cada párrafo debería tener al menos © Universidad Internacional de La Rioja (UNiR) tres oraciones. Elimina frases superfluas y repeticiones de ideas. Instrucciones para la redacción y elaboración del TfE 7 Máster Universitario en Inteligencia Artificial\n"
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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref:\n",
|
||
"- 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. Repasa las citas bibliográficas. Comprueba que todas ellas son correctas y siguen la normativa que exige la titulación. Asegúrate de que las figuras y las tablas se ven clara y correctamente, e incluyen número y título, así como su procedencia o fuente. Comprueba que los índices se generan correctamente. 1.5. Normativa de citas En esta titulación se cita de acuerdo con la normativa APA. Recuerda que tienes una guía con explicaciones y ejemplos en el apartado Citas y bibliografía del aula virtual: https://bibliografiaycitas.unir.net/ © Universidad Internacional de La Rioja (UNIR) Instrucciones para la redacción y elaboración del TFE 8 Máster Universitario en Inteligencia Artificial\n",
|
||
"easy_text:\n",
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||
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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref:\n",
|
||
"2. Estructura del documento En esta sección se describe con mayor profundidad la estructura y los contenidos esperados en cada apartado de tu TFE. Léela con detenimiento y compárala con la programación semanal que encontrarás en el aula virtual, pues en cada borrador deberás entregar completados diferentes apartados que se explican a continuación, y que se elaboran de una manera no necesariamente lineal. Como ya se ha mencionado, la memoria debe estar estructurada en capítulos. Por norma general, la estructura de capítulos suele reflejar la línea de discurso del trabajo, empezando por una introducción donde se plantea el problema, seguida de un estudio de la literatura donde se estudia y describe el contexto. Posteriormente se establecen claramente la hipótesis de trabajo y los objetivos concretos de investigación, así como la descripción de la metodología seguida para alcanzar los objetivos. Posteriormente se describe la contribución del trabajo, seguida de una evaluación de la misma. La evaluación da pie a la elaboración de las conclusiones, que deben relacionar los resultados obtenidos con los objetivos planteados inicialmente. Finalmente, se describen las líneas de trabajo futuro necesarias para seguir avanzando hacia la consecución de los objetivos. A continuación, te dejamos algunos consejos generales sobre cómo organizar los capítulos, pero ten en cuenta que cada trabajo es único y esta organización es una guía general adaptable. El director específico de tu TFE podrá aportarte consejos sobre cómo organizar la memoria adaptándote al contexto de tu trabajo concreto. © Universidad Internacional de La Rioja (UNIR) Como recomendación general, la estructura de capítulos de tu memoria debería ser similar a la siguiente propuesta: Organización del trabajo en grupo (solo en trabajos grupales) Capítulo 1 – Introducción Instrucciones para la redacción y elaboración del TFE 9 Máster Universitario en Inteligencia Artificial\n",
|
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"easy_text:\n",
|
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"2. Estructura del documento En esta sección se describe con mayor profundidad la estructura y los contenidos esperados en cada apartado de tu TFE. Léela con detenimiento y compárala con la programación semanal que encontrarás en el aula virtual, pues en cada borrador deberás entregar completados diferentes apartados que se explican a continuación, Y que se elaboran de una manera no necesariamente lineal. Como ya se ha mencionado, la memoria debe estar estructurada en capítulos. Por norma general, la estructura de capítulos suele reflejar la línea de discurso del trabajo; empezando por una introducción donde se plantea el problema; seguida de un estudio de la literatura donde se estudia Y describe el contexto. Posteriormente se establecen claramente la hipótesis de trabajo Y los objetivos concretos de investigación; así como la descripción de la metodología seguida para alcanzar los objetivos. Posteriormente se describe la contribución del trabajo, seguida de una evaluación de la misma. La evaluación da pie a la elaboración de las conclusiones, que deben relacionar los resultados obtenidos con los objetivos planteados inicialmente. Finalmente, se describen las líneas de trabajo futuro necesarias para seguir avanzando hacia la consecución de los objetivos. 4 continuación, te dejamos algunos consejos generales sobre cómo organizar los capítulos, pero ten en cuenta que cada trabajo es único y esta organización es una guía general adaptable. El director específico de tu TFE podrá aportarte consejos sobre cómo organizar la memoria adaptándote al contexto de tu trabajo concreto. 2 Como recomendación general; la estructura de capítulos de tu memoria debería ser ; 9 similar a la siguiente propuesta: g 1 Organización del trabajo en grupo (solo en trabajos grupales) Capítulo 1 ~ Introducción 1 Instrucciones para la redacción y elaboración del TFE 0 Máster Universitario en Inteligencia Artificial\n",
|
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"docTR_text:\n",
|
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"2. Estructura del documento En esta seccion se describe con mayor protundidad la estructura y los contenidos esperados en cada apartado de tu TFE. Léela con detenimiento y comparala con la programacion semanal que encontraras en el aula virtual, pues en cada borrador deberas entregar completados diferentes apartados que se explican a continuacion, y que se elaboran de una manera no necesariamente lineal. Como ya se ha mencionado, la memoria debe estar estructurada en capitulos. Por norma general, la estructura de capitulos suele reflejar la linea de discurso del trabajo, empezando por una introduccion donde se plantea el problema, seguida de un estudio de la literatura donde se estudia y describe el contexto. Posteriormente se establecen claramente la hipotesis de trabajo y los objetivos concretos de investigacion, asi como la descripcion de la metodologia seguida para alcanzar los objetivos. Posteriormente se describe la contribucion del trabajo, seguida de una evaluacion de la misma. La evaluacion da pie a la elaboracion de las conclusiones, que deben relacionar los resultados obtenidos con los objetivos planteados inicialmente. Finalmente, se describen las lineas de trabajo futuro necesarias para seguir avanzando hacia la consecucion de los objetivos. A continuacion, te dejamos algunos consejos generales sobre como organizar los capitulos, pero ten en cuenta que cada trabajo es unico y esta organizacion es una guia general adaptable. El director especifico de tu TFE podra aportarte consejos sobre como organizar la memoria adaptandote al contexto de tu trabajo concreto. Como recomendacion general, la estructura de capitulos de tu memoria deberia ser similar a la siguiente propuesta: Organizacion del trabajo en grupo (solo en trabajos grupales) Capitulo 1 IN Introduccion Instrucciones para la redaccion y elaboracion del TFE 9 Master Universitario en Inteligencia Artificial\n",
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"paddle_text:\n",
|
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"2.E Estructura del documento En esta sección se describe con mayor profundidad la estructura y los contenidos esperados en cada apartado de tu Tfe. Léela con detenimiento y compárala con la programación semanal que encontraras en el aula virtual, pues en cada borrador deberás entregar completados diferentes apartados que se explican a continuación,y que se elaboran de una manera no necesariamente lineal. Como ya se ha mencionado, la memoria debe estar estructurada en capítulos. Por norma general, la estructura de capitulos suele reflejar la linea de discurso del trabajo, empezando por una introducción donde se plantea el problema, seguida de un estudio de la literatura donde se estudia y describe el contexto. Posteriormente se establecen claramente la hipótesis de trabajo y los objetivos concretos de investigación, así como la descripción de la metodología seguida para alcanzar los objetivos. Posteriormente se describe la contribución del trabajo, seguida de una evaluación de la misma. La evaluación da pie a la elaboración de las conclusiones, que deben relacionar los resultados obtenidos con los objetivos planteados inicialmente. Finalmente, se describen las líneas de trabajo futuro necesarias para seguir avanzando hacia la consecución de los objetivos. A continuación, te dejamos algunos consejos generales sobre cómo organizar los capítulos, pero ten en cuenta que cada trabajo es único y esta organización es una guía general adaptable. El director especifico de tu TFE podrá aportarte consejos sobre cómo organizar la memoria adaptándote al contexto de tu trabajo concreto. Como recomendación general, la estructura de capítulos de tu memoria debería ser similar a la siguiente propuesta: © Universidad Internacional de La Rioja (UNiR) Organización del trabajo en grupo (solo en trabajos grupales) Capítulo1–Introducción Instrucciones para la redacción y elaboración del TFE Máster Universitario en Inteligencia Artificial 6\n"
|
||
]
|
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},
|
||
{
|
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"data": {
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",
|
||
"text/plain": [
|
||
"<Figure size 372.15x526.2 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ref:\n",
|
||
"Capítulo 2 – Contexto y estado del arte Capítulo 3 – Objetivos y metodología de trabajo Capítulo 4 – capítulo de desarrollo de la contribución, título del capítulo dependiendo de la tipología del trabajo Capítulo 5 – capítulo de desarrollo de la contribución, título del capítulo dependiendo de la tipología del trabajo Capítulo 6 – capítulo de desarrollo de la contribución, título del capítulo dependiendo de la tipología del trabajo Capítulo 7 – Conclusiones y trabajo futuro 2.1. Resumen El resumen se redacta en último lugar ya que recoge las contribuciones más importantes del trabajo. Es necesario tener muy clara y completa del documento para poder resumirlo correctamente. Tendrá una extensión de 150 a 300 palabras y deberá ofrecer una visión global de lo que el lector encontrará en el trabajo, destacando sus aspectos fundamentales. Deberás indicar claramente cuál es el objetivo principal del trabajo, la metodología seguida para alcanzarlo, los resultados obtenidos y la principal conclusión alcanzada. A continuación, indicarás de 3 a 5 palabras clave o keywords como descriptores del trabajo que lo enmarcan en unas temáticas determinadas. Serán los utilizados para localizar tu trabajo si llega a ser publicado. © Universidad Internacional de La Rioja (UNIR) Instrucciones para la redacción y elaboración del TFE 10 Máster Universitario en Inteligencia Artificial\n",
|
||
"easy_text:\n",
|
||
"Capítulo 2 = Contexto y estado del arte Capítulo 3 - Objetivos y metodología de trabajo Capítulo 4 capítulo de desarrollo de la contribución, título del capítulo dependiendo de la tipología del trabajo Capítulo 5 capítulo de desarrollo de la contribución, título del capítulo dependiendo de la tipología del trabajo Capítulo 6 capítulo de desarrollo de la contribución, título del capítulo dependiendo de la tipología del trabajo Capítulo 7 ~ Conclusiones Y trabajo futuro 2.1. Resumen EI resumen se redacta en último lugar ya que recoge las contribuciones más importantes del trabajo. Es necesario tener muy clara y completa del documento para poder resumirlo correctamente. Tendrá una extensión de 150 a 300 palabras y deberá ofrecer una visión global de lo que el lector encontrará en el trabajo, destacando sUS aspectos fundamentales. Deberás indicar claramente cuál es el objetivo principal del trabajo; la metodología seguida para alcanzarlo, los resultados obtenidos y la principal conclusión alcanzada. continuación, indicarás de 3 a 5 palabras clave 0 keywords como descriptores del trabajo que lo enmarcan en unas temáticas determinadas. Serán los utilizados para localizar tu trabajo si Ilega a ser publicado. 2 ; 9 g 1 1 Instrucciones para la redacción y elaboración del TFE 10 0 Máster Universitario en Inteligencia Artificial\n",
|
||
"docTR_text:\n",
|
||
"Capitulo 2 IN Contexto y estado del arte Capitulo 3 I- Objetivos y metodologia de trabajo Capitulo 4 IN capitulo de desarrollo de la contribucion, titulo del capitulo dependiendo de la tipologia del trabajo Capitulo 5 il capitulo de desarrollo de la contribucion, titulo del capitulo dependiendo de la tipologia del trabajo Capitulo 6 il capitulo de desarrollo de la contribucion, titulo del capitulo dependiendo de la tipologia del trabajo Capitulo 7 = Conclusiones y trabajo futuro 2.1. Resumen El resumen se redacta en ultimo lugar ya que recoge las contribuciones mas importantes deltrabajo. Es necesario tener muy clara y completa del documento para poder resumirlo correctamente. Tendra una extension de 150 a 300 palabras y debera ofrecer una vision global de lo que el lector encontrara en el trabajo, destacando sus aspectos fundamentales. Deberas indicar claramente cual es el objetivo principal del trabajo, la metodologia seguida para alcanzarlo, los resultados obtenidos y la principal conclusion alcanzada. A continuacion, indicaras de 3 a 5 palabras clave O keywords como descriptores del trabajo que lo enmarcan en unas tematicas determinadas. Seran los utilizados para localizar tu trabajo si llega a ser publicado. Instrucciones para la redaccion y elaboracion del TFE 10 Master Universitario en Inteligencia Artificial\n",
|
||
"paddle_text:\n",
|
||
"Capitulo 2 – Contexto y estado del arte Capítulo 3 – Objetivos y metodología de trabajo Capítulo 4 – capítulo de desarrollo de la contribución, título del capítulo dependiendo de la tipología del trabajo Capítulo 5 – capítulo de desarrollo de la contribución, título del capítulo dependiendo de la tipología del trabajo Capítulo 6 – capítulo de desarrollo de la contribución, título del capítulo dependiendo de la tipología del trabajo Capítulo 7 – Conclusiones y trabajo futuro 2.1. Resumen El resumen se redacta en último lugar ya que recoge las contribuciones más importantes del trabajo. Es necesario tener muy clara y completa del documento para poder resumirlo correctamente. Tendrá una extensión de 150 a 300 palabras y deberá ofrecer una visión global de lo que el lector encontrará en el trabajo,destacando sus aspectos fundamentales. Deberás indicar claramente cuál es el objetivo principal del trabajo, la metodología seguida para alcanzarlo, los resultados obtenidos y la principal conclusión alcanzada. A continuación, indicarás de 3 a 5 palabras clave o keywords como descriptores del trabajo que lo enmarcan en unas temáticas determinadas. Serán los utilizados para localizar tu trabajo si llega a ser publicado. © Universidad Internacional de La Rioja (UNIR) Instrucciones para la redacción y elaboración del TFE 10 Máster Universitario en lnteligencia Artificial\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"\n",
|
||
"results = []\n",
|
||
"\n",
|
||
"for pdf_file in os.listdir(PDF_FOLDER):\n",
|
||
" if not pdf_file.lower().endswith('.pdf'):\n",
|
||
" continue\n",
|
||
" pdf_path = os.path.join(PDF_FOLDER, pdf_file)\n",
|
||
" images = pdf_to_images(pdf_path)\n",
|
||
" \n",
|
||
" for i, img in enumerate(images):\n",
|
||
" if i == 5:\n",
|
||
" break\n",
|
||
" page_num = i+1\n",
|
||
" ref = pdf_extract_text(pdf_path, page_num=page_num)\n",
|
||
" show_page(img, f\"page: {page_num}\")\n",
|
||
" print(f\"ref:\\n{ref}\")\n",
|
||
" \n",
|
||
" # Convert PIL image to numpy array\n",
|
||
" image_array = np.array(img)\n",
|
||
" # EasyOCR\n",
|
||
" easy_text = ocr_easyocr(img, image_array)\n",
|
||
" print(f\"easy_text:\\n{easy_text}\")\n",
|
||
" results.append({'PDF': pdf_file, 'Page': page_num, 'Model': 'EasyOCR', 'Prediction': easy_text, **evaluate_text(ref, easy_text)})\n",
|
||
" \n",
|
||
" # TrOCR\n",
|
||
" doctr_text = ocr_doctr(img, image_array)\n",
|
||
" print(f\"docTR_text:\\n{doctr_text}\")\n",
|
||
" results.append({'PDF': pdf_file, 'Page': page_num, 'Model': 'docTR', 'Prediction': doctr_text, **evaluate_text(ref, doctr_text)})\n",
|
||
" \n",
|
||
" # PaddleOCR\n",
|
||
" paddle_text = ocr_paddle(img, image_array)\n",
|
||
" print(f\"paddle_text:\\n{paddle_text}\")\n",
|
||
" results.append({'PDF': pdf_file, 'Page': page_num, 'Model': 'PaddleOCR', 'Prediction': paddle_text, **evaluate_text(ref, paddle_text)})\n",
|
||
" "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0db6dc74",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 5 Save and Analyze Results"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"id": "da3155e3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Benchmark results saved!\n",
|
||
" WER CER\n",
|
||
"Model \n",
|
||
"EasyOCR 0.101418 0.048243\n",
|
||
"PaddleOCR 0.088599 0.053441\n",
|
||
"docTR 0.182897 0.055500\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_results = pd.DataFrame(results)\n",
|
||
"df_results.to_csv(os.path.join(OUTPUT_FOLDER, 'ai_ocr_benchmark_results.csv'), index=False)\n",
|
||
"print('Benchmark results saved!')\n",
|
||
"\n",
|
||
"# Summary by model\n",
|
||
"summary = df_results.groupby('Model')[['WER', 'CER']].mean()\n",
|
||
"print(summary)\n",
|
||
"\n",
|
||
"# Plot\n",
|
||
"summary.plot(kind='bar', figsize=(8,5), title='AI OCR Benchmark (WER & CER)')\n",
|
||
"plt.ylabel('Error Rate')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3e0f00c0",
|
||
"metadata": {},
|
||
"source": [
|
||
"### How to read this chart:\n",
|
||
"- CER (Character Error Rate) focus on raw transcription quality\n",
|
||
"- WER (Word Error Rate) penalizes incorrect tokenization or missing spaces\n",
|
||
"- CER and WER are error metrics, which means:\n",
|
||
" - Higher values = worse performance\n",
|
||
" - Lower values = better accuracy"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "41b427d4",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Compared solutions\n",
|
||
"| Model | Type | Components | Key Strengths | Why It Matters |\n",
|
||
"| :--------------------- | :--------------------------- | :--------------------------- | :--------------------------------------------------------- | :------------------------------------------------------- |\n",
|
||
"| **EasyOCR** | End-to-end (det + rec) | DB + CRNN/Transformer | Lightweight, easy to run, multilingual | Serves as *baseline neuronal* (fast & reproducible). |\n",
|
||
"| **PaddleOCR (PP-OCR)** | End-to-end (det + rec + cls) | DB + SRN/CRNN | Strong multilingual support, configurable pipeline | Industrial reference; widely benchmarked. |\n",
|
||
"| **DocTR** | End-to-end (det + rec) | DB/LinkNet + CRNN/SAR/VitSTR | Research-oriented, clean API, high-level structured output | Represents the latest *PyTorch*-based academic approach. |\n",
|
||
"\n",
|
||
"\n",
|
||
"These cover the three major open-source paradigms for deep OCR:\n",
|
||
"\n",
|
||
"EasyOCR: compact CRNN-based recognizer.\n",
|
||
"\n",
|
||
"PaddleOCR: large industrial model (PP-OCR family).\n",
|
||
"\n",
|
||
"DocTR: modular research library from Mindee, built for experimentation.\n",
|
||
"\n",
|
||
"Together they already let you analyse:\n",
|
||
"\n",
|
||
"accuracy (CER/WER),\n",
|
||
"\n",
|
||
"inference latency,\n",
|
||
"\n",
|
||
"model architecture trade-offs."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": ".venv (3.11.9)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.11.9"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|