{ "cells": [ { "cell_type": "markdown", "id": "be3c1872", "metadata": {}, "source": [ "# AI-based OCR Benchmark Notebook\n", "\n", "This notebook benchmarks **AI-based OCR models** on scanned PDF documents/images in Spanish.\n", "It excludes traditional OCR engines like Tesseract that require external installations." ] }, { "cell_type": "code", "execution_count": 1, "id": "6a1e98fe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pip in c:\\users\\sergio\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (25.3)\n", "Note: you may need to restart the kernel to use updated packages.\n", "Requirement already satisfied: jupyter in c:\\users\\sergio\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (1.1.1)\n", "Requirement already satisfied: notebook in c:\\users\\sergio\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from jupyter) (7.5.0)\n", "Requirement already satisfied: 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install --upgrade jupyter\n", "%pip install --upgrade ipywidgets\n", "%pip install --upgrade ipykernel" ] }, { "cell_type": "code", "execution_count": null, "id": "13103c58", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: transformers in c:\\users\\sergio\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (4.57.3)\n", "Requirement already satisfied: pillow in c:\\users\\sergio\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (12.0.0)\n", "Requirement already satisfied: paddleocr in c:\\users\\sergio\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (3.3.2)\n", "Requirement already satisfied: hf_xet in c:\\users\\sergio\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (1.2.0)\n", "Requirement already satisfied: paddlepaddle in c:\\users\\sergio\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (3.2.2)\n", "Requirement already satisfied: 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pandas>=1.2->seaborn) (2025.2)\n", "Requirement already satisfied: six>=1.5 in c:\\users\\sergio\\appdata\\roaming\\python\\python311\\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": [ "# Install necessary packages\n", "%pip install transformers pillow paddleocr hf_xet paddlepaddle jiwer\n", "\n", "\n", "\n", "# Data analysis and visualization\n", "%pip install pandas\n", "%pip install matplotlib\n", "%pip install seaborn" ] }, { "cell_type": "code", "execution_count": 47, "id": "ae33632a", "metadata": {}, "outputs": [], "source": [ "# Imports\n", "import os, json\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "import re\n", "from datetime import datetime" ] }, { "cell_type": "markdown", "id": "0e00f1b0", "metadata": {}, "source": [ "## 1 Configuration" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "PDF_FOLDER = './dataset' # Folder containing PDF files\n", "OUTPUT_FOLDER = 'results'\n", "os.makedirs(OUTPUT_FOLDER, exist_ok=True)" ] }, { "cell_type": "code", "execution_count": 40, "id": "8bd4ca23", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
c:\\Users\\Sergio\\Desktop\\MastersThesis\\dataset\n",
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c:\\Users\\Sergio\\Desktop\\MastersThesis\\paddle_ocr_tuning.py\n",
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c:\\Users\\Sergio\\Desktop\\MastersThesis\n",
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\n" ], "text/plain": [ "c:\\Users\\Sergio\\Desktop\\MastersThesis\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "PDF_FOLDER_ABS = os.path.abspath(PDF_FOLDER) # ./instructions -> C:\\...\\instructions\n", "SCRIPT_ABS = os.path.abspath(\"paddle_ocr_tuning.py\") # paddle_ocr_tuning.py -> C:\\...\\paddle_ocr_tuning.py\n", "SCRIPT_DIR = os.path.dirname(SCRIPT_ABS)\n", "\n", "print(PDF_FOLDER_ABS)\n", "print(SCRIPT_ABS)\n", "print(SCRIPT_DIR)" ] }, { "cell_type": "code", "execution_count": 93, "id": "9c658b58", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Paddle version: 3.2.2\n",
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\n" ], "text/plain": [ "Paddle version: \u001b[1;36m3.2\u001b[0m.\u001b[1;36m2\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
GPU available: False\n",
       "
\n" ], "text/plain": [ "GPU available: \u001b[3;91mFalse\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
GPU count: 0\n",
       "
\n" ], "text/plain": [ "GPU count: \u001b[1;36m0\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Current device: cpu\n",
       "
\n" ], "text/plain": [ "Current device: cpu\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import paddle\n", "\n", "print(\"Paddle version:\", paddle.__version__)\n", "print(\"GPU available:\", paddle.device.is_compiled_with_cuda())\n", "print(\"GPU count:\", paddle.device.cuda.device_count())\n", "print(\"Current device:\", paddle.device.get_device())" ] }, { "cell_type": "code", "execution_count": 41, "id": "243849b9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mCreating model: ('PP-LCNet_x1_0_doc_ori', None)\u001b[0m\n", "\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\Sergio\\.paddlex\\official_models\\PP-LCNet_x1_0_doc_ori`.\u001b[0m\n", "\u001b[32mCreating model: ('UVDoc', None)\u001b[0m\n", "\u001b[32mModel files already exist. Using cached files. To redownload, please delete the directory manually: `C:\\Users\\Sergio\\.paddlex\\official_models\\UVDoc`.\u001b[0m\n", "\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\\Sergio\\.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\\Sergio\\.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\\Sergio\\.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", ")" ] }, { "cell_type": "code", "execution_count": 42, "id": "329da34a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
3.3.2\n",
       "
\n" ], "text/plain": [ "\u001b[1;36m3.3\u001b[0m.\u001b[1;36m2\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import paddleocr\n", "\n", "print(paddleocr.__version__)" ] }, { "cell_type": "code", "execution_count": 43, "id": "b1541bb6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
c:\\Users\\Sergio\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\paddleocr\n",
       "
\n" ], "text/plain": [ "c:\\Users\\Sergio\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\paddleocr\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1) Locate the installed PaddleOCR package\n", "pkg_dir = os.path.dirname(paddleocr.__file__)\n", "print(pkg_dir)" ] }, { "cell_type": "markdown", "id": "84c999e2", "metadata": {}, "source": [ "## 2 Helper Functions" ] }, { "cell_type": "code", "execution_count": 48, "id": "9596c7df", "metadata": {}, "outputs": [], "source": [ "from typing import List, Optional\n", "from paddle_ocr_tuning import evaluate_text, assemble_from_paddle_result\n", "from dataset_manager import ImageTextDataset" ] }, { "cell_type": "code", "execution_count": 37, "id": "b7c1bbf8", "metadata": {}, "outputs": [], "source": [ "from PIL import Image\n", "\n", "def show_page(img: Image.Image, scale: float = 1):\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()" ] }, { "cell_type": "code", "execution_count": 44, "id": "b9d3fe25", "metadata": {}, "outputs": [ { "data": { "image/png": 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Índice\n",
       "1. Indicaciones generales 3\n",
       "1.1. Línea de discurso 3\n",
       "1.2. Estructura general y extensión del TFE 4\n",
       "1.3. Formatos y plantilla de trabajo 5\n",
       "1.4. Estética y estilo de redacción 7\n",
       "1.5. Normativa de citas 8\n",
       "2. Estructura del documento 9\n",
       "2.1. Resumen 10\n",
       "2.2. Organización del trabajo en grupo 11\n",
       "2.3. Introducción 11\n",
       "2.4. Contexto y estado del arte 13\n",
       "2.5. Objetivos concretos y metodología de trabajo 14\n",
       "2.6. Desarrollo específico de la contribución 17\n",
       "2.7. Conclusiones y trabajo futuro 20\n",
       "2.8. Referencias bibliográficas 21\n",
       "© Universidad Internacional de La Rioja (UNIR)\n",
       "2.8.1. Herramientas para buscar bibliografía 22\n",
       "2.9. Anexos 23\n",
       "2.10. Índice de acrónimos 24\n",
       "
\n" ], "text/plain": [ "Índice\n", "\u001b[1;36m1\u001b[0m. Indicaciones generales \u001b[1;36m3\u001b[0m\n", "\u001b[1;36m1.1\u001b[0m. Línea de discurso \u001b[1;36m3\u001b[0m\n", "\u001b[1;36m1.2\u001b[0m. Estructura general y extensión del TFE \u001b[1;36m4\u001b[0m\n", "\u001b[1;36m1.3\u001b[0m. Formatos y plantilla de trabajo \u001b[1;36m5\u001b[0m\n", "\u001b[1;36m1.4\u001b[0m. Estética y estilo de redacción \u001b[1;36m7\u001b[0m\n", "\u001b[1;36m1.5\u001b[0m. Normativa de citas \u001b[1;36m8\u001b[0m\n", "\u001b[1;36m2\u001b[0m. Estructura del documento \u001b[1;36m9\u001b[0m\n", "\u001b[1;36m2.1\u001b[0m. Resumen \u001b[1;36m10\u001b[0m\n", "\u001b[1;36m2.2\u001b[0m. Organización del trabajo en grupo \u001b[1;36m11\u001b[0m\n", "\u001b[1;36m2.3\u001b[0m. Introducción \u001b[1;36m11\u001b[0m\n", "\u001b[1;36m2.4\u001b[0m. Contexto y estado del arte \u001b[1;36m13\u001b[0m\n", "\u001b[1;36m2.5\u001b[0m. Objetivos concretos y metodología de trabajo \u001b[1;36m14\u001b[0m\n", "\u001b[1;36m2.6\u001b[0m. Desarrollo específico de la contribución \u001b[1;36m17\u001b[0m\n", "\u001b[1;36m2.7\u001b[0m. Conclusiones y trabajo futuro \u001b[1;36m20\u001b[0m\n", "\u001b[1;36m2.8\u001b[0m. Referencias bibliográficas \u001b[1;36m21\u001b[0m\n", "© Universidad Internacional de La Rioja \u001b[1m(\u001b[0mUNIR\u001b[1m)\u001b[0m\n", "\u001b[1;36m2.8\u001b[0m.\u001b[1;36m1\u001b[0m. Herramientas para buscar bibliografía \u001b[1;36m22\u001b[0m\n", "\u001b[1;36m2.9\u001b[0m. Anexos \u001b[1;36m23\u001b[0m\n", "\u001b[1;36m2.10\u001b[0m. Índice de acrónimos \u001b[1;36m24\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#test\n", "dataset = ImageTextDataset(PDF_FOLDER_ABS)\n", "img, txt = dataset[1]\n", "show_page(img, 0.15)\n", "print(txt)" ] }, { "cell_type": "code", "execution_count": 45, "id": "dcd27755", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Superior e inferior: 2,5 cm.\n",
       "Formato de párrafo en texto principal (estilo de la plantilla “Normal”):\n",
       " Calibri 12, justificado, interlineado 1,5, espacio entre párrafos 6 puntos\n",
       "anterior y 6 puntos posterior, sin sangría.\n",
       "Títulos:\n",
       " Primer nivel (estilo de la plantilla “Título 1”): Calibri Light 18, azul, justificado,\n",
       "interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos\n",
       "posterior, sin sangría.\n",
       " Segundo nivel (estilo de la plantilla “Título 2”): Calibri Light 14, azul,\n",
       "justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6\n",
       "puntos posterior, sin sangría.\n",
       " Tercer nivel (estilo de la plantilla “Título 3”: Calibri Light 12, justificado,\n",
       "interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos\n",
       "posterior, sin sangría.\n",
       "Notas al pie:\n",
       " Calibri 10, justificado, interlineado sencillo, espacio entre párrafos 0 puntos\n",
       "anterior y 0 puntos posterior, sin sangría.\n",
       "Tablas y figuras:\n",
       " Título en la parte superior de la tabla o figura.\n",
       " Numeración tabla o figura (Tabla 1/ Figura1): Calibri 12, negrita, justificado.\n",
       " Nombre tabla o figura: Calibri 12, cursiva, justificado.\n",
       " Cuerpo: la tipografía de las tablas o figuras se pueden reducir hasta los 9\n",
       "puntos si estas contienen mucha información. Si la tabla o figura es muy\n",
       "grande, también se puede colocar en apaisado dentro de la hoja.\n",
       " Fuente de la tabla o figura en la parte inferior. Calibri 9,5, centrado.\n",
       "Encabezado y pie de página:\n",
       " Todas las páginas llevarán un encabezado con el nombre completo del\n",
       "estudiante y el título del TFE.\n",
       "© Universidad Internacional de La Rioja (UNIR)\n",
       " Todas las páginas llevarán también un pie de página con el número de página.\n",
       "Instrucciones para la redacción y elaboración del TFE\n",
       "6\n",
       "Máster Universitario en Inteligencia Artificial\n",
       "
\n" ], "text/plain": [ "Superior e inferior: \u001b[1;36m2\u001b[0m,\u001b[1;36m5\u001b[0m cm.\n", "Formato de párrafo en texto principal \u001b[1m(\u001b[0mestilo de la plantilla “Normal”\u001b[1m)\u001b[0m:\n", " Calibri \u001b[1;36m12\u001b[0m, justificado, interlineado \u001b[1;36m1\u001b[0m,\u001b[1;36m5\u001b[0m, espacio entre párrafos \u001b[1;36m6\u001b[0m puntos\n", "anterior y \u001b[1;36m6\u001b[0m puntos posterior, sin sangría.\n", "Títulos:\n", " Primer nivel \u001b[1m(\u001b[0mestilo de la plantilla “Título \u001b[1;36m1\u001b[0m”\u001b[1m)\u001b[0m: Calibri Light \u001b[1;36m18\u001b[0m, azul, justificado,\n", "interlineado \u001b[1;36m1\u001b[0m,\u001b[1;36m5\u001b[0m, espacio entre párrafos \u001b[1;36m6\u001b[0m puntos anterior y \u001b[1;36m6\u001b[0m puntos\n", "posterior, sin sangría.\n", " Segundo nivel \u001b[1m(\u001b[0mestilo de la plantilla “Título \u001b[1;36m2\u001b[0m”\u001b[1m)\u001b[0m: Calibri Light \u001b[1;36m14\u001b[0m, azul,\n", "justificado, interlineado \u001b[1;36m1\u001b[0m,\u001b[1;36m5\u001b[0m, espacio entre párrafos \u001b[1;36m6\u001b[0m puntos anterior y \u001b[1;36m6\u001b[0m\n", "puntos posterior, sin sangría.\n", " Tercer nivel \u001b[1m(\u001b[0mestilo de la plantilla “Título \u001b[1;36m3\u001b[0m”: Calibri Light \u001b[1;36m12\u001b[0m, justificado,\n", "interlineado \u001b[1;36m1\u001b[0m,\u001b[1;36m5\u001b[0m, espacio entre párrafos \u001b[1;36m6\u001b[0m puntos anterior y \u001b[1;36m6\u001b[0m puntos\n", "posterior, sin sangría.\n", "Notas al pie:\n", " Calibri \u001b[1;36m10\u001b[0m, justificado, interlineado sencillo, espacio entre párrafos \u001b[1;36m0\u001b[0m puntos\n", "anterior y \u001b[1;36m0\u001b[0m puntos posterior, sin sangría.\n", "Tablas y figuras:\n", " Título en la parte superior de la tabla o figura.\n", " Numeración tabla o figura \u001b[1m(\u001b[0mTabla \u001b[1;36m1\u001b[0m/ Figura1\u001b[1m)\u001b[0m: Calibri \u001b[1;36m12\u001b[0m, negrita, justificado.\n", " Nombre tabla o figura: Calibri \u001b[1;36m12\u001b[0m, cursiva, justificado.\n", " Cuerpo: la tipografía de las tablas o figuras se pueden reducir hasta los \u001b[1;36m9\u001b[0m\n", "puntos si estas contienen mucha información. Si la tabla o figura es muy\n", "grande, también se puede colocar en apaisado dentro de la hoja.\n", " Fuente de la tabla o figura en la parte inferior. Calibri \u001b[1;36m9\u001b[0m,\u001b[1;36m5\u001b[0m, centrado.\n", "Encabezado y pie de página:\n", " Todas las páginas llevarán un encabezado con el nombre completo del\n", "estudiante y el título del TFE.\n", "© Universidad Internacional de La Rioja \u001b[1m(\u001b[0mUNIR\u001b[1m)\u001b[0m\n", " Todas las páginas llevarán también un pie de página con el número de página.\n", "Instrucciones para la redacción y elaboración del TFE\n", "\u001b[1;36m6\u001b[0m\n", "Máster Universitario en Inteligencia Artificial\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset = ImageTextDataset(PDF_FOLDER_ABS)\n", "img, txt = dataset[5]\n", "show_page(img, 0.15)\n", "print(txt)" ] }, { "cell_type": "markdown", "id": "e42cae29", "metadata": {}, "source": [ "## Run AI OCR Benchmark" ] }, { "cell_type": "code", "execution_count": 23, "id": "9b55c154", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "ref: \n", "Superior e inferior: 2,5 cm.\n", "Formato de párrafo en texto principal (estilo de la plantilla “Normal”):\n", " Calibri 12, justificado, interlineado 1,5, espacio entre párrafos 6 puntos\n", "anterior y 6 puntos posterior, sin sangría.\n", "Títulos:\n", " Primer nivel (estilo de la plantilla “Título 1”): Calibri Light 18, azul, justificado,\n", "interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos\n", "posterior, sin sangría.\n", " Segundo nivel (estilo de la plantilla “Título 2”): Calibri Light 14, azul,\n", "justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6\n", "puntos posterior, sin sangría.\n", " Tercer nivel (estilo de la plantilla “Título 3”: Calibri Light 12, justificado,\n", "interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6 puntos\n", "posterior, sin sangría.\n", "Notas al pie:\n", " Calibri 10, justificado, interlineado sencillo, espacio entre párrafos 0 puntos\n", "anterior y 0 puntos posterior, sin sangría.\n", "Tablas y figuras:\n", " Título en la parte superior de la tabla o figura.\n", " Numeración tabla o figura (Tabla 1/ Figura1): Calibri 12, negrita, justificado.\n", " Nombre tabla o figura: Calibri 12, cursiva, justificado.\n", " Cuerpo: la tipografía de las tablas o figuras se pueden reducir hasta los 9\n", "puntos si estas contienen mucha información. Si la tabla o figura es muy\n", "grande, también se puede colocar en apaisado dentro de la hoja.\n", " Fuente de la tabla o figura en la parte inferior. Calibri 9,5, centrado.\n", "Encabezado y pie de página:\n", " Todas las páginas llevarán un encabezado con el nombre completo del\n", "estudiante y el título del TFE.\n", "© Universidad Internacional de La Rioja (UNIR)\n", " Todas las páginas llevarán también un pie de página con el número de página.\n", "Instrucciones para la redacción y elaboración del TFE\n", "6\n", "Máster Universitario en Inteligencia Artificial\n", "paddle_text: \n", "Superior e inferior: 2,5 cm.\n", "Formato de párrafo en texto principal (estilo de la plantilla “Normal\"):\n", "Calibri 12, justificado, interlineado 1,5, espacio entre párrafos 6 puntos\n", "anterior y 6 puntos posterior, sin sangría.\n", "Títulos:\n", "Primer nivel (estilo de la plantillaTítulo 1\"): Calibri Light 18, azul, justificado,\n", "interlineado 1,5,espacio entre párrafos 6 puntos anterior y 6 puntos\n", "posterior, sin sangría.\n", "Segundo nivel (estilo de la plantilla Titulo 2\"): Calibri Light 14, azul,\n", "justificado, interlineado 1,5, espacio entre párrafos 6 puntos anterior y 6\n", "puntos posterior, sin sangría.\n", "Tercer nivel (estilo de la plantilla Título 3\": Calibri Light 12, justificado,\n", "interlineado 1,5,espacio entre párrafos 6 puntos anterior y 6 puntos\n", "posterior, sin sangría.\n", "Notas al pie:\n", "Calibri 10, justificado, interlineado sencillo, espacio entre párrafos O puntos\n", "anterior y O puntos posterior, sin sangra.\n", "Tablas y figuras:\n", "Título en la parte superior de la tabla o figura.\n", "Numeración tabla o figura (Tabla 1/ Figura1): Calibri 12, negrita, justificado.\n", "Nombre tabla o figura: Calibri 12, cursiva, justificado.\n", "Cuerpo: la tipografía de las tablas o figuras se pueden reducir hasta los 9\n", "puntos si estas contienen mucha información. Si la tabla o figura es muy\n", "grande, también se puede colocar en apaisado dentro de la hoja.\n", "Fuente de la tabla o figura en la parte inferior. Calibri 9,5, centrado.\n", "Encabezado y pie de página:\n", "Todas las páginas llevarán un encabezado con el nombre completo del\n", "estudiante y el título del TFE.\n", "© Universidad Internacional de La Rioja (UNiR)\n", "Todas las páginas llevarán también un pie de página con el número de página.\n", "Instrucciones para la redacción y elaboración del TFE\n", "Máster Universitario en Inteligencia Artificial 9\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "ref: \n", "Los borradores intermedios deberán entregarse en formato Word. El documento final\n", "deberá depositarse en formato PDF.\n", "1.4. Estética y estilo de redacción\n", "Es fundamental que el TFE presente un aspecto elegante y correcto. Se trata de un\n", "trabajo académico y debe reflejar la madurez y el nivel formativo de una persona que\n", "ha finalizado un estudio de grado o postgrado. Ten en cuenta las siguientes\n", "recomendaciones en todas y cada una de las entregas que realices y, en especial, en\n", "el depósito final del documento:\n", " Verifica la originalidad del documento, asegurándote de que citas todas las\n", "fuentes consultadas y no existen textos de autoría ajena sin referenciar\n", "correctamente.\n", " Cuida la presentación del trabajo. Comprueba que formatos como tipo y tamaño\n", "de letra, número de páginas, encabezados, justificación de párrafos, interlineado,\n", "etc., son correctos.\n", " Revisa la ortografía y la redacción. Utiliza el corrector de Word para asegurarte de\n", "que no has dejado ninguna errata. Una lectura detenida del documento también\n", "te ayudará a detectar erratas, omisiones o redundancias. Si es posible, pide a\n", "alguien cercano que lo lea y te dé su opinión sobre la redacción. Presta especial\n", "atención a los siguientes aspectos:\n", "- Que los párrafos sigan un orden o hilo argumental lógico.\n", "- Que la información se presente de una manera que facilite su\n", "comprensión, definiendo los conceptos necesarios e incluyendo las citas\n", "bibliográficas pertinentes.\n", "- Elimina párrafos demasiado cortos. Cada párrafo debería tener al menos\n", "© Universidad Internacional de La Rioja (UNIR)\n", "tres oraciones.\n", "- Elimina frases superfluas y repeticiones de ideas.\n", "Instrucciones para la redacción y elaboración del TFE\n", "7\n", "Máster Universitario en Inteligencia Artificial\n", "paddle_text: \n", "Los borradores intermedios deberán entregarse en formato Word. El documento final\n", "deberá depositarse en formato PDf.\n", "1.4. Estética y estilo de redacción\n", "Es fundamental que el TFE presente un aspecto elegante y correcto. Se trata de un\n", "trabajo académico y debe reflejar la madurez y el nivel formativo de una persona que\n", "ha finalizado un estudio de grado o postgrado. Ten en cuenta las siguientes\n", "recomendaciones en todas y cada una de las entregas que realices y, en especial, en\n", "el deposito final del documento:\n", "Verifica la originalidad del documento,asegurándote de que citas todas las\n", "fuentes consultadas y no existen textos de autoría ajena sin referenciar\n", "correctamente.\n", "Cuida la presentación del trabajo. Comprueba que formatos como tipo y tamaño\n", "de letra, número de páginas, encabezados, justificación de párrafos, interlineado,\n", "etc., son correctos.\n", "Revisa la ortografía y la redacción. Utiliza el corrector de Word para asegurarte de\n", "que no has dejado ninguna errata. Una lectura detenida del documento también\n", "te ayudará a detectar erratas, omisiones o redundancias. Si es posible, pide a\n", "alguien cercano que lo lea y te dé su opinión sobre la redacción. Presta especial\n", "atención a los siguientes aspectos:\n", "Que los párrafos sigan un orden o hilo argumental lógico.\n", "Que la información se presente de una manera que facilite su\n", "comprensión, definiendo los conceptos necesarios e incluyendo las citas\n", "bibliograficas pertinentes.\n", "Elimina párrafos demasiado cortos. Cada párrafo debería tener al menos\n", "© Universidad Internacional de La Rioja (UNiR) tres oraciones.\n", "Elimina frases superfluas y repeticiones de ideas.\n", "Instrucciones para la redacción y elaboración del TfE 7\n", "Máster Universitario en Inteligencia Artificial\n" ] }, { "data": { "image/png": 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", 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" ] }, "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\n", "apartado, explicando de qué vas a tratar en esa sección. Evita que\n", "aparezcan dos encabezados de nivel consecutivos sin ningún texto entre\n", "medias.\n", " Repasa las citas bibliográficas. Comprueba que todas ellas son correctas y siguen\n", "la normativa que exige la titulación.\n", " Asegúrate de que las figuras y las tablas se ven clara y correctamente, e incluyen\n", "número y título, así como su procedencia o fuente.\n", " Comprueba que los índices se generan correctamente.\n", "1.5. Normativa de citas\n", "En esta titulación se cita de acuerdo con la normativa APA.\n", "Recuerda que tienes una guía con explicaciones y ejemplos en el apartado Citas y\n", "bibliografía del aula virtual: https://bibliografiaycitas.unir.net/\n", "© Universidad Internacional de La Rioja (UNIR)\n", "Instrucciones para la redacción y elaboración del TFE\n", "8\n", "Máster Universitario en Inteligencia Artificial\n", "paddle_text: \n", "Escribe siempre al menos un párrafo de introducción en cada capítulo o\n", "apartado,explicando de qué vas a tratar en esa sección. Evita que\n", "aparezcan dos encabezados de nivel consecutivos sin ningún texto entre\n", "medias.\n", "Repasa las citas bibliográficas. Comprueba que todas ellas son correctas y siguen\n", "la normativa que exige la titulación.\n", "Asegúrate de que las figuras y las tablas se ven clara y correctamente, e incluyen\n", "número y título, así como su procedencia o fuente.\n", "Comprueba que los índices se generan correctamente.\n", "1.5. Normativa adecitas\n", "En esta titulacióon se cita de acuerdo con la normativa Apa.\n", "Recuerda que tienes una guía con explicaciones y ejemplos en el apartado Citas y\n", "bibliografía del aula virtual: https://bibliografiaycitas.unir.net/\n", "© Universidad Internacional de La Rioja (UNIR)\n", "Instrucciones para la redacción y elaboración del TfE\n", "Máster Universitario en lnteligencia Artificial ∞\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "ref: \n", "2. Estructura del documento\n", "En esta sección se describe con mayor profundidad la estructura y los contenidos\n", "esperados en cada apartado de tu TFE.\n", "Léela con detenimiento y compárala con la programación semanal que encontrarás\n", "en el aula virtual, pues en cada borrador deberás entregar completados diferentes\n", "apartados que se explican a continuación, y que se elaboran de una manera no\n", "necesariamente lineal.\n", "Como ya se ha mencionado, la memoria debe estar estructurada en capítulos. Por\n", "norma general, la estructura de capítulos suele reflejar la línea de discurso del\n", "trabajo, empezando por una introducción donde se plantea el problema, seguida de\n", "un estudio de la literatura donde se estudia y describe el contexto. Posteriormente\n", "se establecen claramente la hipótesis de trabajo y los objetivos concretos de\n", "investigación, así como la descripción de la metodología seguida para alcanzar los\n", "objetivos. Posteriormente se describe la contribución del trabajo, seguida de una\n", "evaluación de la misma. La evaluación da pie a la elaboración de las conclusiones,\n", "que deben relacionar los resultados obtenidos con los objetivos planteados\n", "inicialmente. Finalmente, se describen las líneas de trabajo futuro necesarias para\n", "seguir avanzando hacia la consecución de los objetivos.\n", "A continuación, te dejamos algunos consejos generales sobre cómo organizar los\n", "capítulos, pero ten en cuenta que cada trabajo es único y esta organización es una\n", "guía general adaptable. El director específico de tu TFE podrá aportarte consejos\n", "sobre cómo organizar la memoria adaptándote al contexto de tu trabajo concreto.\n", "Como recomendación general, la estructura de capítulos de tu memoria debería ser\n", "similar a la siguiente propuesta:\n", "© Universidad Internacional de La Rioja (UNIR)\n", " Organización del trabajo en grupo (solo en trabajos grupales)\n", " Capítulo 1 – Introducción\n", "Instrucciones para la redacción y elaboración del TFE\n", "9\n", "Máster Universitario en Inteligencia Artificial\n", "paddle_text: \n", "2.E Estructura del documento\n", "En esta sección se describe con mayor profundidad la estructura y los contenidos\n", "esperados en cada apartado de tu Tfe.\n", "Léela con detenimiento y compárala con la programación semanal que encontraras\n", "en el aula virtual, pues en cada borrador deberás entregar completados diferentes\n", "apartados que se explican a continuación,y que se elaboran de una manera no\n", "necesariamente lineal.\n", "Como ya se ha mencionado, la memoria debe estar estructurada en capítulos. Por\n", "norma general, la estructura de capitulos suele reflejar la linea de discurso del\n", "trabajo, empezando por una introducción donde se plantea el problema, seguida de\n", "un estudio de la literatura donde se estudia y describe el contexto. Posteriormente\n", "se establecen claramente la hipótesis de trabajo y los objetivos concretos de\n", "investigación, así como la descripción de la metodología seguida para alcanzar los\n", "objetivos. Posteriormente se describe la contribución del trabajo, seguida de una\n", "evaluación de la misma. La evaluación da pie a la elaboración de las conclusiones,\n", "que deben relacionar los resultados obtenidos con los objetivos planteados\n", "inicialmente. Finalmente, se describen las líneas de trabajo futuro necesarias para\n", "seguir avanzando hacia la consecución de los objetivos.\n", "A continuación, te dejamos algunos consejos generales sobre cómo organizar los\n", "capítulos, pero ten en cuenta que cada trabajo es único y esta organización es una\n", "guía general adaptable. El director especifico de tu TFE podrá aportarte consejos\n", "sobre cómo organizar la memoria adaptándote al contexto de tu trabajo concreto.\n", "Como recomendación general, la estructura de capítulos de tu memoria debería ser\n", "similar a la siguiente propuesta:\n", "© Universidad Internacional de La Rioja (UNiR)\n", "Organización del trabajo en grupo (solo en trabajos grupales)\n", "Capítulo1–Introducción\n", "Instrucciones para la redacción y elaboración del TFE\n", "Máster Universitario en Inteligencia Artificial 6\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "ref: \n", "Capítulo 2 – Contexto y estado del arte\n", " Capítulo 3 – Objetivos y metodología de trabajo\n", " Capítulo 4 – capítulo de desarrollo de la contribución, título del capítulo\n", "dependiendo de la tipología del trabajo\n", " Capítulo 5 – capítulo de desarrollo de la contribución, título del capítulo\n", "dependiendo de la tipología del trabajo\n", " Capítulo 6 – capítulo de desarrollo de la contribución, título del capítulo\n", "dependiendo de la tipología del trabajo\n", " Capítulo 7 – Conclusiones y trabajo futuro\n", "2.1. Resumen\n", "El resumen se redacta en último lugar ya que recoge las contribuciones más\n", "importantes del trabajo. Es necesario tener muy clara y completa del documento para\n", "poder resumirlo correctamente.\n", "Tendrá una extensión de 150 a 300 palabras y deberá ofrecer una visión global de lo\n", "que el lector encontrará en el trabajo, destacando sus aspectos fundamentales.\n", "Deberás indicar claramente cuál es el objetivo principal del trabajo, la metodología\n", "seguida para alcanzarlo, los resultados obtenidos y la principal conclusión alcanzada.\n", "A continuación, indicarás de 3 a 5 palabras clave o keywords como descriptores del\n", "trabajo que lo enmarcan en unas temáticas determinadas. Serán los utilizados para\n", "localizar tu trabajo si llega a ser publicado.\n", "© Universidad Internacional de La Rioja (UNIR)\n", "Instrucciones para la redacción y elaboración del TFE\n", "10\n", "Máster Universitario en Inteligencia Artificial\n", "paddle_text: \n", "Capitulo 2 – Contexto y estado del arte\n", "Capítulo 3 – Objetivos y metodología de trabajo\n", "Capítulo 4 – capítulo de desarrollo de la contribución, título del capítulo\n", "dependiendo de la tipología del trabajo\n", "Capítulo 5 – capítulo de desarrollo de la contribución, título del capítulo\n", "dependiendo de la tipología del trabajo\n", "Capítulo 6 – capítulo de desarrollo de la contribución, título del capítulo\n", "dependiendo de la tipología del trabajo\n", "Capítulo 7 – Conclusiones y trabajo futuro\n", "2.1. Resumen\n", "El resumen se redacta en último lugar ya que recoge las contribuciones más\n", "importantes del trabajo. Es necesario tener muy clara y completa del documento para\n", "poder resumirlo correctamente.\n", "Tendrá una extensión de 150 a 300 palabras y deberá ofrecer una visión global de lo\n", "que el lector encontrará en el trabajo,destacando sus aspectos fundamentales.\n", "Deberás indicar claramente cuál es el objetivo principal del trabajo, la metodología\n", "seguida para alcanzarlo, los resultados obtenidos y la principal conclusión alcanzada.\n", "A continuación, indicarás de 3 a 5 palabras clave o keywords como descriptores del\n", "trabajo que lo enmarcan en unas temáticas determinadas. Serán los utilizados para\n", "localizar tu trabajo si llega a ser publicado.\n", "© Universidad Internacional de La Rioja (UNIR)\n", "Instrucciones para la redacción y elaboración del TFE 10\n", "Máster Universitario en lnteligencia Artificial\n" ] } ], "source": [ "from itertools import islice\n", "\n", "results = []\n", "for img, txt in islice(dataset, 5, 10):\n", " image_array = np.array(img)\n", " out = paddleocr_model.predict(\n", " image_array,\n", " use_doc_orientation_classify=False,\n", " use_doc_unwarping=False,\n", " use_textline_orientation=True\n", " )\n", " show_page(img, 0.15)\n", " print(f\"ref: \\n{txt}\")\n", " paddle_text = assemble_from_paddle_result(out)\n", " print(f\"paddle_text: \\n{paddle_text}\")\n", " results.append({'Model': 'PaddleOCR', 'Prediction': paddle_text, **evaluate_text(txt, paddle_text)})\n", " " ] }, { "cell_type": "markdown", "id": "0db6dc74", "metadata": {}, "source": [ "## 5 Save and Analyze Results" ] }, { "cell_type": "code", "execution_count": 24, "id": "da3155e3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Benchmark results saved as ai_ocr_benchmark_finetune_results_20251206_200806.csv\n", " WER CER\n", "Model \n", "PaddleOCR 0.104067 0.012581\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_results = pd.DataFrame(results)\n", "\n", "# Generate a unique filename with timestamp\n", "timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n", "filename = f\"ai_ocr_benchmark_finetune_results_{timestamp}.csv\"\n", "filepath = os.path.join(OUTPUT_FOLDER, filename)\n", "\n", "df_results.to_csv(filepath, index=False)\n", "print(f\"Benchmark results saved as {filename}\")\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": "830b0e25", "metadata": {}, "source": [ "# Busqueda de hyperparametros\n", "https://docs.ray.io/en/latest/tune/index.html" ] }, { "cell_type": "code", "execution_count": 25, "id": "3a4bd700", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python 3.11.9\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\"pip\" no se reconoce como un comando interno o externo,\n", "programa o archivo por lotes ejecutable.\n" ] } ], "source": [ "!python --version\n", "!pip --version" ] }, { "cell_type": "code", "execution_count": 26, "id": "b0cf4bcf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: rich in c:\\users\\sergio\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (14.2.0)\n", "Requirement already satisfied: ray[tune] 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"Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\sergio\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->ray[tune]) (2025.11.12)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "# Instalación de Ray y Ray Tune\n", "%pip install -U \"ray[tune]\" rich" ] }, { "cell_type": "code", "execution_count": 27, "id": "f3ca0b9b", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-12-06 20:08:33,299\tINFO worker.py:2023 -- Started a local Ray instance.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Ray Tune listo (versión: 2.52.1 )\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\Sergio\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ray\\_private\\worker.py:2062: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0\n", " warnings.warn(\n" ] } ], "source": [ "import ray\n", "from ray import tune\n", "from ray.tune.schedulers import ASHAScheduler\n", "\n", "ray.init(ignore_reinit_error=True)\n", "print(\"Ray Tune listo (versión:\", ray.__version__, \")\")" ] }, { "cell_type": "code", "execution_count": 28, "id": "ae5a10c4", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-12-06 20:08:38,850\tINFO worker.py:1855 -- Calling ray.init() again after it has already been called.\n" ] } ], "source": [ "# ===============================================================\n", "# 🔍 RAY TUNE: OPTIMIZACIÓN AUTOMÁTICA DE HIPERPARÁMETROS OCR\n", "# ===============================================================\n", "\n", "from ray import tune, air\n", "from ray.tune.schedulers import ASHAScheduler\n", "import pandas as pd\n", "import time\n", "import colorama\n", "from rich import print\n", "import sys, subprocess \n", "from rich.console import Console\n", "\n", "colorama.just_fix_windows_console()\n", "ray.init(ignore_reinit_error=True)\n", "\n", "# Tell Ray Tune to use a Jupyter-compatible console\n", "console = Console(force_jupyter=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "96c320e8", "metadata": {}, "outputs": [], "source": [ "\n", "\n", "# --- Configuración base del experimento ---\n", "search_space = {\n", " \"textline_orientation\": tune.choice([True, False]),\n", " \"text_det_box_thresh\": tune.uniform(0.4, 0.7),\n", " \"text_det_unclip_ratio\": tune.uniform(1.0, 2.0),\n", " \"text_rec_score_thresh\": tune.uniform(0, 1.0),\n", " \"line_tolerance\": tune.uniform(0, 2.0),\n", " \"min_box_score\": tune.uniform(0, 1.0)\n", "}\n", "KEYMAP = {\n", " \"textline_orientation\": \"textline-orientation\",\n", " \"text_det_box_thresh\": \"text-det-box-thresh\",\n", " \"text_det_unclip_ratio\": \"text-det-unclip-ratio\",\n", " \"text_rec_score_thresh\": \"text-rec-score-thresh\",\n", " \"line_tolerance\": \"line-tolerance\",\n", " \"min_box_score\": \"min-box-score\",\n", "}" ] }, { "cell_type": "code", "execution_count": 51, "id": "accb4e9d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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args: ['c:\\\\Users\\\\Sergio\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python311\\\\python.exe', \n",
       "'c:\\\\Users\\\\Sergio\\\\Desktop\\\\MastersThesis\\\\paddle_ocr_tuning.py', '--pdf-folder', \n",
       "'c:\\\\Users\\\\Sergio\\\\Desktop\\\\MastersThesis\\\\dataset', '--textline-orientation', 'True', '--text-det-box-thresh', \n",
       "'0.46611732611383844', '--text-det-unclip-ratio', '1.3598680409827462', '--text-rec-score-thresh', '0.0', \n",
       "'--line-tolerance', '0.5', '--min-box-score', '0.6']\n",
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Tune Status

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Current time:2025-12-06 22:38:36
Running for: 01:41:47.58
Memory: 4.5/15.9 GiB
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System Info

\n", " Using AsyncHyperBand: num_stopped=13
Bracket: Iter 64.000: None | Iter 32.000: None | Iter 16.000: None | Iter 8.000: None | Iter 4.000: None | Iter 2.000: None | Iter 1.000: -0.11205841913079691
Logical resource usage: 1.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)\n", "
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Trial Status

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Trial name status loc line_tolerance min_box_score text_det_box_thresh text_det_unclip_rati\n", "o text_rec_score_thres\n", "htextline_orientation iter total time (s) CER WER TIME
trainable_paddle_ocr_b3bdc_00000TERMINATED127.0.0.1:19504 0.7 0 0.5652971.282490.2True 1 399.5250.06391320.148775376.277
trainable_paddle_ocr_b3bdc_00001TERMINATED127.0.0.1:18012 0.7 0 0.6107611.788240 True 1 386.4870.13589 0.304316362.611
trainable_paddle_ocr_b3bdc_00002TERMINATED127.0.0.1:10864 0.6 0 0.5329721.9115 0.4False 1 377.0080.125817 0.356445351.958
trainable_paddle_ocr_b3bdc_00003TERMINATED127.0.0.1:11400 0.7 0.5 0.6633 1.695260.4False 1 373.1720.152864 0.2617 349.995
trainable_paddle_ocr_b3bdc_00004TERMINATED127.0.0.1:16556 0.6 0 0.5050191.882710.4False 1 379.8860.127304 0.342154355.984
trainable_paddle_ocr_b3bdc_00005TERMINATED127.0.0.1:12240 0.7 0.5 0.60097 1.6219 0.4True 1 382.5450.09812260.237748360.209
trainable_paddle_ocr_b3bdc_00006TERMINATED127.0.0.1:19712 0.7 0.5 0.4545681.359530.2False 1 397.7640.06298210.151528373.757
trainable_paddle_ocr_b3bdc_00007TERMINATED127.0.0.1:17768 0.6 0.5 0.6493151.695950.2True 1 385.2290.125408 0.252182362.127
trainable_paddle_ocr_b3bdc_00008TERMINATED127.0.0.1:14292 0.6 0.5 0.5146541.822850.4False 1 380.6470.113582 0.308721356.781
trainable_paddle_ocr_b3bdc_00009TERMINATED127.0.0.1:7292 0.6 0.6 0.6190981.790970.4False 1 375.6860.141796 0.301124352.926
trainable_paddle_ocr_b3bdc_00010TERMINATED127.0.0.1:22764 0.7 0.5 0.47992 1.648770 False 1 385.4060.08279620.218071361.063
trainable_paddle_ocr_b3bdc_00011TERMINATED127.0.0.1:6256 0.7 0.6 0.6730781.631110 True 1 377.8390.190876 0.292151353.255
trainable_paddle_ocr_b3bdc_00012TERMINATED127.0.0.1:12344 0.5 0.6 0.4971961.313680.4True 1 393.1020.065148 0.162362368.717
trainable_paddle_ocr_b3bdc_00013TERMINATED127.0.0.1:15216 0.6 0.6 0.5925431.3073 0.2True 1 391.8360.06416230.155348369.637
trainable_paddle_ocr_b3bdc_00014TERMINATED127.0.0.1:22580 0.7 0 0.6694691.853260.2False 1 358.2560.207922 0.354745335.335
trainable_paddle_ocr_b3bdc_00015TERMINATED127.0.0.1:23532 0.6 0.5 0.4460471.228360 False 1 380.0530.06346270.150588359.242
trainable_paddle_ocr_b3bdc_00016TERMINATED127.0.0.1:4760 0.6 0.5 0.4646921.653760.2False 1 366.5380.07256270.212321343.623
trainable_paddle_ocr_b3bdc_00017TERMINATED127.0.0.1:10784 0.7 0.6 0.57992 1.408870 True 1 369.3780.06798260.163942347.845
trainable_paddle_ocr_b3bdc_00018TERMINATED127.0.0.1:10972 0.7 0 0.6987421.640070.2False 1 352.4810.212477 0.32237 331.44
trainable_paddle_ocr_b3bdc_00019TERMINATED127.0.0.1:4780 0.5 0.5 0.6516561.205430 False 1 370.6790.126101 0.21466 349.24
trainable_paddle_ocr_b3bdc_00020TERMINATED127.0.0.1:20080 0.5 0.6 0.5562851.645390 False 1 365.1020.08233360.214002343.422
trainable_paddle_ocr_b3bdc_00021TERMINATED127.0.0.1:1072 0.6 0.5 0.5566931.592570.4True 1 371.4780.08044530.205515349.989
trainable_paddle_ocr_b3bdc_00022TERMINATED127.0.0.1:19888 0.7 0 0.4255311.325310.4False 1 376.6790.06319580.14949 354.681
trainable_paddle_ocr_b3bdc_00023TERMINATED127.0.0.1:18380 0.6 0 0.49713 1.788140.2False 1 368.4290.09891760.278952346.66
trainable_paddle_ocr_b3bdc_00024TERMINATED127.0.0.1:10164 0.5 0.5 0.4561051.928520.2False 1 362.5730.138896 0.371172340.864
trainable_paddle_ocr_b3bdc_00025TERMINATED127.0.0.1:10396 0.6 0.6 0.6772631.407550.2True 1 367.9180.185939 0.280449346.613
trainable_paddle_ocr_b3bdc_00026TERMINATED127.0.0.1:1824 0.5 0 0.6926371.203170.4True 1 369.1470.198069 0.289923347.498
trainable_paddle_ocr_b3bdc_00027TERMINATED127.0.0.1:21808 0.7 0.6 0.6211161.853430.4True 1 361.9010.156036 0.331298340.435
trainable_paddle_ocr_b3bdc_00028TERMINATED127.0.0.1:19872 0.6 0.6 0.5298331.261360 False 1 377.2970.063679 0.154287355.93
trainable_paddle_ocr_b3bdc_00029TERMINATED127.0.0.1:2816 0.6 0.6 0.6074591.644670.4False 1 368.3460.110535 0.249928346.987
trainable_paddle_ocr_b3bdc_00030TERMINATED127.0.0.1:7328 0.7 0.6 0.6545241.434760.4True 1 367.2740.143618 0.235326345.124
trainable_paddle_ocr_b3bdc_00031TERMINATED127.0.0.1:11640 0.5 0 0.4511051.353360 True 1 373.7460.06400480.152993352.525
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Trial Progress

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Trial name CER PAGES TIME TIME_PER_PAGE WER
trainable_paddle_ocr_b3bdc_000000.0639132 5376.277 75.14850.148775
trainable_paddle_ocr_b3bdc_000010.13589 5362.611 72.40620.304316
trainable_paddle_ocr_b3bdc_000020.125817 5351.958 70.28870.356445
trainable_paddle_ocr_b3bdc_000030.152864 5349.995 69.89530.2617
trainable_paddle_ocr_b3bdc_000040.127304 5355.984 71.08980.342154
trainable_paddle_ocr_b3bdc_000050.0981226 5360.209 71.94280.237748
trainable_paddle_ocr_b3bdc_000060.0629821 5373.757 74.64480.151528
trainable_paddle_ocr_b3bdc_000070.125408 5362.127 72.31590.252182
trainable_paddle_ocr_b3bdc_000080.113582 5356.781 71.24290.308721
trainable_paddle_ocr_b3bdc_000090.141796 5352.926 70.46940.301124
trainable_paddle_ocr_b3bdc_000100.0827962 5361.063 72.10060.218071
trainable_paddle_ocr_b3bdc_000110.190876 5353.255 70.53770.292151
trainable_paddle_ocr_b3bdc_000120.065148 5368.717 73.63290.162362
trainable_paddle_ocr_b3bdc_000130.0641623 5369.637 73.82440.155348
trainable_paddle_ocr_b3bdc_000140.207922 5335.335 66.95990.354745
trainable_paddle_ocr_b3bdc_000150.0634627 5359.242 71.73560.150588
trainable_paddle_ocr_b3bdc_000160.0725627 5343.623 68.63450.212321
trainable_paddle_ocr_b3bdc_000170.0679826 5347.845 69.47630.163942
trainable_paddle_ocr_b3bdc_000180.212477 5331.44 66.19460.32237
trainable_paddle_ocr_b3bdc_000190.126101 5349.24 69.748 0.21466
trainable_paddle_ocr_b3bdc_000200.0823336 5343.422 68.59030.214002
trainable_paddle_ocr_b3bdc_000210.0804453 5349.989 69.89620.205515
trainable_paddle_ocr_b3bdc_000220.0631958 5354.681 70.83880.14949
trainable_paddle_ocr_b3bdc_000230.0989176 5346.66 69.23140.278952
trainable_paddle_ocr_b3bdc_000240.138896 5340.864 68.075 0.371172
trainable_paddle_ocr_b3bdc_000250.185939 5346.613 69.22950.280449
trainable_paddle_ocr_b3bdc_000260.198069 5347.498 69.39910.289923
trainable_paddle_ocr_b3bdc_000270.156036 5340.435 67.98820.331298
trainable_paddle_ocr_b3bdc_000280.063679 5355.93 71.08910.154287
trainable_paddle_ocr_b3bdc_000290.110535 5346.987 69.288 0.249928
trainable_paddle_ocr_b3bdc_000300.143618 5345.124 68.92890.235326
trainable_paddle_ocr_b3bdc_000310.0640048 5352.525 70.41770.152993
\n", "
\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "2025-12-06 21:03:20,722\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00001_1_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6108,text_det_unclip_ratio=1.7882,t_2025-12-06_20-56-49\n", "2025-12-06 21:03:20,823\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00002_2_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5330,text_det_unclip_ratio=1.9115,t_2025-12-06_21-03-20\n", "2025-12-06 21:03:20,826\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00002_2_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5330,text_det_unclip_ratio=1.9115,t_2025-12-06_21-03-20\n", "2025-12-06 21:03:27,092\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00002_2_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5330,text_det_unclip_ratio=1.9115,t_2025-12-06_21-03-20\n", "2025-12-06 21:03:27,093\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00002_2_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5330,text_det_unclip_ratio=1.9115,t_2025-12-06_21-03-20\n", "2025-12-06 21:03:33,718\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00000_0_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.5653,text_det_unclip_ratio=1.2825,t_2025-12-06_20-56-49\n", "2025-12-06 21:03:33,736\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00003_3_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6633,text_det_unclip_ratio=1.6_2025-12-06_21-03-33\n", "2025-12-06 21:03:33,737\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00003_3_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6633,text_det_unclip_ratio=1.6_2025-12-06_21-03-33\n", "2025-12-06 21:03:38,480\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00003_3_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6633,text_det_unclip_ratio=1.6_2025-12-06_21-03-33\n", "2025-12-06 21:03:38,481\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00003_3_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6633,text_det_unclip_ratio=1.6_2025-12-06_21-03-33\n", "\u001b[36m(trainable_paddle_ocr pid=10864)\u001b[0m [2025-12-06 21:03:56,519 E 10864 15180] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n", "\u001b[36m(trainable_paddle_ocr pid=11400)\u001b[0m [2025-12-06 21:04:08,749 E 11400 18988] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 21:09:44,135\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00002_2_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5330,text_det_unclip_ratio=1.9115,t_2025-12-06_21-03-20\n", "2025-12-06 21:09:44,171\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00004_4_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5050,text_det_unclip_ratio=1.8827,t_2025-12-06_21-09-44\n", "2025-12-06 21:09:44,175\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00004_4_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5050,text_det_unclip_ratio=1.8827,t_2025-12-06_21-09-44\n", "2025-12-06 21:09:49,719\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00004_4_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5050,text_det_unclip_ratio=1.8827,t_2025-12-06_21-09-44\n", "2025-12-06 21:09:49,722\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00004_4_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5050,text_det_unclip_ratio=1.8827,t_2025-12-06_21-09-44\n", "2025-12-06 21:09:51,685\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00003_3_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6633,text_det_unclip_ratio=1.6_2025-12-06_21-03-33\n", "2025-12-06 21:09:51,694\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00005_5_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6010,text_det_unclip_ratio=1.6_2025-12-06_21-09-51\n", "2025-12-06 21:09:51,696\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00005_5_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6010,text_det_unclip_ratio=1.6_2025-12-06_21-09-51\n", "2025-12-06 21:09:56,292\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00005_5_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6010,text_det_unclip_ratio=1.6_2025-12-06_21-09-51\n", "2025-12-06 21:09:56,293\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00005_5_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6010,text_det_unclip_ratio=1.6_2025-12-06_21-09-51\n", "\u001b[36m(trainable_paddle_ocr pid=16556)\u001b[0m [2025-12-06 21:10:19,454 E 16556 7328] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "\u001b[36m(trainable_paddle_ocr pid=12240)\u001b[0m [2025-12-06 21:10:26,611 E 12240 18476] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 21:16:09,646\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00004_4_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.5050,text_det_unclip_ratio=1.8827,t_2025-12-06_21-09-44\n", "2025-12-06 21:16:09,711\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00006_6_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4546,text_det_unclip_ratio=1.3_2025-12-06_21-16-09\n", "2025-12-06 21:16:09,713\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00006_6_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4546,text_det_unclip_ratio=1.3_2025-12-06_21-16-09\n", "2025-12-06 21:16:15,640\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00006_6_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4546,text_det_unclip_ratio=1.3_2025-12-06_21-16-09\n", "2025-12-06 21:16:15,642\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00006_6_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4546,text_det_unclip_ratio=1.3_2025-12-06_21-16-09\n", "2025-12-06 21:16:18,859\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00005_5_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.6010,text_det_unclip_ratio=1.6_2025-12-06_21-09-51\n", "2025-12-06 21:16:18,876\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00007_7_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.6493,text_det_unclip_ratio=1.6_2025-12-06_21-16-18\n", "2025-12-06 21:16:18,876\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00007_7_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.6493,text_det_unclip_ratio=1.6_2025-12-06_21-16-18\n", "2025-12-06 21:16:23,437\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00007_7_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.6493,text_det_unclip_ratio=1.6_2025-12-06_21-16-18\n", "2025-12-06 21:16:23,440\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00007_7_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.6493,text_det_unclip_ratio=1.6_2025-12-06_21-16-18\n", "\u001b[36m(trainable_paddle_ocr pid=19712)\u001b[0m [2025-12-06 21:16:45,168 E 19712 3960] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "\u001b[36m(trainable_paddle_ocr pid=17768)\u001b[0m [2025-12-06 21:16:53,820 E 17768 20672] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 21:22:48,714\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00007_7_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.6493,text_det_unclip_ratio=1.6_2025-12-06_21-16-18\n", "2025-12-06 21:22:48,768\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00008_8_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5147,text_det_unclip_ratio=1.8_2025-12-06_21-22-48\n", "2025-12-06 21:22:48,771\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00008_8_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5147,text_det_unclip_ratio=1.8_2025-12-06_21-22-48\n", "2025-12-06 21:22:53,439\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00006_6_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4546,text_det_unclip_ratio=1.3_2025-12-06_21-16-09\n", "2025-12-06 21:22:53,461\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00009_9_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6191,text_det_unclip_ratio=1.7_2025-12-06_21-22-53\n", "2025-12-06 21:22:53,462\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00009_9_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6191,text_det_unclip_ratio=1.7_2025-12-06_21-22-53\n", "2025-12-06 21:22:54,552\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00008_8_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5147,text_det_unclip_ratio=1.8_2025-12-06_21-22-48\n", "2025-12-06 21:22:54,553\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00008_8_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5147,text_det_unclip_ratio=1.8_2025-12-06_21-22-48\n", "2025-12-06 21:22:58,237\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00009_9_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6191,text_det_unclip_ratio=1.7_2025-12-06_21-22-53\n", "2025-12-06 21:22:58,238\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00009_9_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6191,text_det_unclip_ratio=1.7_2025-12-06_21-22-53\n", "\u001b[36m(trainable_paddle_ocr pid=14292)\u001b[0m [2025-12-06 21:23:24,260 E 14292 17720] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 21:29:13,968\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00009_9_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6191,text_det_unclip_ratio=1.7_2025-12-06_21-22-53\n", "2025-12-06 21:29:14,001\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00010_10_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4799,text_det_unclip_ratio=1._2025-12-06_21-29-14\n", "2025-12-06 21:29:14,003\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00010_10_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4799,text_det_unclip_ratio=1._2025-12-06_21-29-14\n", "2025-12-06 21:29:15,230\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00008_8_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5147,text_det_unclip_ratio=1.8_2025-12-06_21-22-48\n", "2025-12-06 21:29:15,252\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00011_11_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6731,text_det_unclip_ratio=1._2025-12-06_21-29-15\n", "2025-12-06 21:29:15,253\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00011_11_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6731,text_det_unclip_ratio=1._2025-12-06_21-29-15\n", "2025-12-06 21:29:19,725\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00010_10_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4799,text_det_unclip_ratio=1._2025-12-06_21-29-14\n", "2025-12-06 21:29:19,725\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00010_10_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4799,text_det_unclip_ratio=1._2025-12-06_21-29-14\n", "2025-12-06 21:29:19,956\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00011_11_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6731,text_det_unclip_ratio=1._2025-12-06_21-29-15\n", "2025-12-06 21:29:19,958\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00011_11_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6731,text_det_unclip_ratio=1._2025-12-06_21-29-15\n", "\u001b[36m(trainable_paddle_ocr pid=22764)\u001b[0m [2025-12-06 21:29:49,308 E 22764 6536] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n", "2025-12-06 21:35:37,866\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00011_11_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6731,text_det_unclip_ratio=1._2025-12-06_21-29-15\n", "2025-12-06 21:35:37,911\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00012_12_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.4972,text_det_unclip_ratio=1._2025-12-06_21-35-37\n", "2025-12-06 21:35:37,915\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00012_12_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.4972,text_det_unclip_ratio=1._2025-12-06_21-35-37\n", "2025-12-06 21:35:43,961\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00012_12_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.4972,text_det_unclip_ratio=1._2025-12-06_21-35-37\n", "2025-12-06 21:35:43,963\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00012_12_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.4972,text_det_unclip_ratio=1._2025-12-06_21-35-37\n", "2025-12-06 21:35:45,167\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00010_10_line_tolerance=0.7000,min_box_score=0.5000,text_det_box_thresh=0.4799,text_det_unclip_ratio=1._2025-12-06_21-29-14\n", "2025-12-06 21:35:45,186\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00013_13_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5925,text_det_unclip_ratio=1._2025-12-06_21-35-45\n", "2025-12-06 21:35:45,194\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00013_13_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5925,text_det_unclip_ratio=1._2025-12-06_21-35-45\n", "2025-12-06 21:35:49,781\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00013_13_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5925,text_det_unclip_ratio=1._2025-12-06_21-35-45\n", "2025-12-06 21:35:49,782\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00013_13_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5925,text_det_unclip_ratio=1._2025-12-06_21-35-45\n", "\u001b[36m(trainable_paddle_ocr pid=12344)\u001b[0m [2025-12-06 21:36:14,512 E 12344 12400] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n", "\u001b[36m(trainable_paddle_ocr pid=15216)\u001b[0m [2025-12-06 21:36:20,204 E 15216 20000] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 21:42:17,137\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00012_12_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.4972,text_det_unclip_ratio=1._2025-12-06_21-35-37\n", "2025-12-06 21:42:17,239\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00014_14_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6695,text_det_unclip_ratio=1.8533,_2025-12-06_21-42-17\n", "2025-12-06 21:42:17,242\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00014_14_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6695,text_det_unclip_ratio=1.8533,_2025-12-06_21-42-17\n", "2025-12-06 21:42:21,653\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00013_13_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5925,text_det_unclip_ratio=1._2025-12-06_21-35-45\n", "2025-12-06 21:42:21,673\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00015_15_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4460,text_det_unclip_ratio=1._2025-12-06_21-42-21\n", "2025-12-06 21:42:21,675\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00015_15_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4460,text_det_unclip_ratio=1._2025-12-06_21-42-21\n", "2025-12-06 21:42:23,303\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00014_14_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6695,text_det_unclip_ratio=1.8533,_2025-12-06_21-42-17\n", "2025-12-06 21:42:23,303\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00014_14_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6695,text_det_unclip_ratio=1.8533,_2025-12-06_21-42-17\n", "2025-12-06 21:42:26,244\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00015_15_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4460,text_det_unclip_ratio=1._2025-12-06_21-42-21\n", "2025-12-06 21:42:26,252\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00015_15_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4460,text_det_unclip_ratio=1._2025-12-06_21-42-21\n", "\u001b[36m(trainable_paddle_ocr pid=22580)\u001b[0m [2025-12-06 21:42:53,892 E 22580 16980] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 21:48:21,584\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00014_14_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6695,text_det_unclip_ratio=1.8533,_2025-12-06_21-42-17\n", "2025-12-06 21:48:21,613\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00016_16_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4647,text_det_unclip_ratio=1._2025-12-06_21-48-21\n", "2025-12-06 21:48:21,616\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00016_16_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4647,text_det_unclip_ratio=1._2025-12-06_21-48-21\n", "2025-12-06 21:48:27,021\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00016_16_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4647,text_det_unclip_ratio=1._2025-12-06_21-48-21\n", "2025-12-06 21:48:27,022\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00016_16_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4647,text_det_unclip_ratio=1._2025-12-06_21-48-21\n", "2025-12-06 21:48:46,315\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00015_15_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4460,text_det_unclip_ratio=1._2025-12-06_21-42-21\n", "2025-12-06 21:48:46,330\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00017_17_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.5799,text_det_unclip_ratio=1._2025-12-06_21-48-46\n", "2025-12-06 21:48:46,334\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00017_17_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.5799,text_det_unclip_ratio=1._2025-12-06_21-48-46\n", "2025-12-06 21:48:51,241\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00017_17_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.5799,text_det_unclip_ratio=1._2025-12-06_21-48-46\n", "2025-12-06 21:48:51,245\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00017_17_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.5799,text_det_unclip_ratio=1._2025-12-06_21-48-46\n", "\u001b[36m(trainable_paddle_ocr pid=4760)\u001b[0m [2025-12-06 21:48:56,886 E 4760 14816] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\u001b[32m [repeated 2x across cluster]\u001b[0m\n", "\u001b[36m(trainable_paddle_ocr pid=10784)\u001b[0m [2025-12-06 21:49:21,382 E 10784 20052] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 21:54:33,574\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00016_16_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.4647,text_det_unclip_ratio=1._2025-12-06_21-48-21\n", "2025-12-06 21:54:33,590\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00018_18_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6987,text_det_unclip_ratio=1.6401,_2025-12-06_21-54-33\n", "2025-12-06 21:54:33,592\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00018_18_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6987,text_det_unclip_ratio=1.6401,_2025-12-06_21-54-33\n", "2025-12-06 21:54:38,335\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00018_18_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6987,text_det_unclip_ratio=1.6401,_2025-12-06_21-54-33\n", "2025-12-06 21:54:38,336\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00018_18_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6987,text_det_unclip_ratio=1.6401,_2025-12-06_21-54-33\n", "2025-12-06 21:55:00,634\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00017_17_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.5799,text_det_unclip_ratio=1._2025-12-06_21-48-46\n", "2025-12-06 21:55:00,660\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00019_19_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.6517,text_det_unclip_ratio=1._2025-12-06_21-55-00\n", "2025-12-06 21:55:00,665\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00019_19_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.6517,text_det_unclip_ratio=1._2025-12-06_21-55-00\n", "2025-12-06 21:55:05,476\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00019_19_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.6517,text_det_unclip_ratio=1._2025-12-06_21-55-00\n", "2025-12-06 21:55:05,478\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00019_19_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.6517,text_det_unclip_ratio=1._2025-12-06_21-55-00\n", "\u001b[36m(trainable_paddle_ocr pid=10972)\u001b[0m [2025-12-06 21:55:08,599 E 10972 6384] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "\u001b[36m(trainable_paddle_ocr pid=4780)\u001b[0m [2025-12-06 21:55:35,787 E 4780 4064] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:00:30,830\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00018_18_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.6987,text_det_unclip_ratio=1.6401,_2025-12-06_21-54-33\n", "2025-12-06 22:00:30,842\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00020_20_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.5563,text_det_unclip_ratio=1._2025-12-06_22-00-30\n", "2025-12-06 22:00:30,845\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00020_20_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.5563,text_det_unclip_ratio=1._2025-12-06_22-00-30\n", "2025-12-06 22:00:35,845\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00020_20_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.5563,text_det_unclip_ratio=1._2025-12-06_22-00-30\n", "2025-12-06 22:00:35,847\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00020_20_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.5563,text_det_unclip_ratio=1._2025-12-06_22-00-30\n", "\u001b[36m(trainable_paddle_ocr pid=20080)\u001b[0m [2025-12-06 22:01:06,051 E 20080 21004] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:01:16,163\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00019_19_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.6517,text_det_unclip_ratio=1._2025-12-06_21-55-00\n", "2025-12-06 22:01:16,176\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00021_21_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5567,text_det_unclip_ratio=1._2025-12-06_22-01-16\n", "2025-12-06 22:01:16,178\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00021_21_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5567,text_det_unclip_ratio=1._2025-12-06_22-01-16\n", "2025-12-06 22:01:20,878\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00021_21_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5567,text_det_unclip_ratio=1._2025-12-06_22-01-16\n", "2025-12-06 22:01:20,880\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00021_21_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5567,text_det_unclip_ratio=1._2025-12-06_22-01-16\n", "\u001b[36m(trainable_paddle_ocr pid=1072)\u001b[0m [2025-12-06 22:01:51,143 E 1072 22252] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:06:40,951\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00020_20_line_tolerance=0.5000,min_box_score=0.6000,text_det_box_thresh=0.5563,text_det_unclip_ratio=1._2025-12-06_22-00-30\n", "2025-12-06 22:06:40,972\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00022_22_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.4255,text_det_unclip_ratio=1.3253,_2025-12-06_22-06-40\n", "2025-12-06 22:06:40,972\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00022_22_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.4255,text_det_unclip_ratio=1.3253,_2025-12-06_22-06-40\n", "2025-12-06 22:06:45,826\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00022_22_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.4255,text_det_unclip_ratio=1.3253,_2025-12-06_22-06-40\n", "2025-12-06 22:06:45,826\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00022_22_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.4255,text_det_unclip_ratio=1.3253,_2025-12-06_22-06-40\n", "\u001b[36m(trainable_paddle_ocr pid=19888)\u001b[0m [2025-12-06 22:07:16,150 E 19888 11400] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:07:32,369\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00021_21_line_tolerance=0.6000,min_box_score=0.5000,text_det_box_thresh=0.5567,text_det_unclip_ratio=1._2025-12-06_22-01-16\n", "2025-12-06 22:07:32,382\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00023_23_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.4971,text_det_unclip_ratio=1.7881,_2025-12-06_22-07-32\n", "2025-12-06 22:07:32,384\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00023_23_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.4971,text_det_unclip_ratio=1.7881,_2025-12-06_22-07-32\n", "2025-12-06 22:07:37,267\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00023_23_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.4971,text_det_unclip_ratio=1.7881,_2025-12-06_22-07-32\n", "2025-12-06 22:07:37,269\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00023_23_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.4971,text_det_unclip_ratio=1.7881,_2025-12-06_22-07-32\n", "\u001b[36m(trainable_paddle_ocr pid=18380)\u001b[0m [2025-12-06 22:08:07,587 E 18380 21300] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:13:02,527\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00022_22_line_tolerance=0.7000,min_box_score=0,text_det_box_thresh=0.4255,text_det_unclip_ratio=1.3253,_2025-12-06_22-06-40\n", "2025-12-06 22:13:02,557\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00024_24_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.4561,text_det_unclip_ratio=1._2025-12-06_22-13-02\n", "2025-12-06 22:13:02,560\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00024_24_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.4561,text_det_unclip_ratio=1._2025-12-06_22-13-02\n", "2025-12-06 22:13:07,568\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00024_24_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.4561,text_det_unclip_ratio=1._2025-12-06_22-13-02\n", "2025-12-06 22:13:07,569\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00024_24_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.4561,text_det_unclip_ratio=1._2025-12-06_22-13-02\n", "\u001b[36m(trainable_paddle_ocr pid=10164)\u001b[0m [2025-12-06 22:13:37,764 E 10164 21820] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:13:45,715\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00023_23_line_tolerance=0.6000,min_box_score=0,text_det_box_thresh=0.4971,text_det_unclip_ratio=1.7881,_2025-12-06_22-07-32\n", "2025-12-06 22:13:45,728\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00025_25_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6773,text_det_unclip_ratio=1._2025-12-06_22-13-45\n", "2025-12-06 22:13:45,728\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00025_25_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6773,text_det_unclip_ratio=1._2025-12-06_22-13-45\n", "2025-12-06 22:13:50,534\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00025_25_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6773,text_det_unclip_ratio=1._2025-12-06_22-13-45\n", "2025-12-06 22:13:50,535\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00025_25_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6773,text_det_unclip_ratio=1._2025-12-06_22-13-45\n", "\u001b[36m(trainable_paddle_ocr pid=10396)\u001b[0m [2025-12-06 22:14:21,005 E 10396 23176] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:19:10,166\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00024_24_line_tolerance=0.5000,min_box_score=0.5000,text_det_box_thresh=0.4561,text_det_unclip_ratio=1._2025-12-06_22-13-02\n", "2025-12-06 22:19:10,172\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00026_26_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.6926,text_det_unclip_ratio=1.2032,_2025-12-06_22-19-10\n", "2025-12-06 22:19:10,177\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00026_26_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.6926,text_det_unclip_ratio=1.2032,_2025-12-06_22-19-10\n", "2025-12-06 22:19:14,972\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00026_26_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.6926,text_det_unclip_ratio=1.2032,_2025-12-06_22-19-10\n", "2025-12-06 22:19:14,972\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00026_26_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.6926,text_det_unclip_ratio=1.2032,_2025-12-06_22-19-10\n", "\u001b[36m(trainable_paddle_ocr pid=1824)\u001b[0m [2025-12-06 22:19:45,228 E 1824 7268] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:19:58,469\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00025_25_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6773,text_det_unclip_ratio=1._2025-12-06_22-13-45\n", "2025-12-06 22:19:58,478\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00027_27_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6211,text_det_unclip_ratio=1._2025-12-06_22-19-58\n", "2025-12-06 22:19:58,481\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00027_27_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6211,text_det_unclip_ratio=1._2025-12-06_22-19-58\n", "2025-12-06 22:20:03,306\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00027_27_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6211,text_det_unclip_ratio=1._2025-12-06_22-19-58\n", "2025-12-06 22:20:03,308\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00027_27_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6211,text_det_unclip_ratio=1._2025-12-06_22-19-58\n", "\u001b[36m(trainable_paddle_ocr pid=21808)\u001b[0m [2025-12-06 22:20:33,554 E 21808 14068] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:25:24,131\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00026_26_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.6926,text_det_unclip_ratio=1.2032,_2025-12-06_22-19-10\n", "2025-12-06 22:25:24,145\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00028_28_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5298,text_det_unclip_ratio=1._2025-12-06_22-25-24\n", "2025-12-06 22:25:24,152\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00028_28_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5298,text_det_unclip_ratio=1._2025-12-06_22-25-24\n", "2025-12-06 22:25:28,966\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00028_28_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5298,text_det_unclip_ratio=1._2025-12-06_22-25-24\n", "2025-12-06 22:25:28,969\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00028_28_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5298,text_det_unclip_ratio=1._2025-12-06_22-25-24\n", "\u001b[36m(trainable_paddle_ocr pid=19872)\u001b[0m [2025-12-06 22:25:59,280 E 19872 19348] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:26:05,219\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00027_27_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6211,text_det_unclip_ratio=1._2025-12-06_22-19-58\n", "2025-12-06 22:26:05,241\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00029_29_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6075,text_det_unclip_ratio=1._2025-12-06_22-26-05\n", "2025-12-06 22:26:05,243\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00029_29_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6075,text_det_unclip_ratio=1._2025-12-06_22-26-05\n", "2025-12-06 22:26:09,991\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00029_29_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6075,text_det_unclip_ratio=1._2025-12-06_22-26-05\n", "2025-12-06 22:26:09,992\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00029_29_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6075,text_det_unclip_ratio=1._2025-12-06_22-26-05\n", "\u001b[36m(trainable_paddle_ocr pid=2816)\u001b[0m [2025-12-06 22:26:40,444 E 2816 12056] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:31:46,277\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00028_28_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.5298,text_det_unclip_ratio=1._2025-12-06_22-25-24\n", "2025-12-06 22:31:46,294\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00030_30_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6545,text_det_unclip_ratio=1._2025-12-06_22-31-46\n", "2025-12-06 22:31:46,297\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00030_30_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6545,text_det_unclip_ratio=1._2025-12-06_22-31-46\n", "2025-12-06 22:31:51,273\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00030_30_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6545,text_det_unclip_ratio=1._2025-12-06_22-31-46\n", "2025-12-06 22:31:51,277\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00030_30_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6545,text_det_unclip_ratio=1._2025-12-06_22-31-46\n", "2025-12-06 22:32:18,349\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00029_29_line_tolerance=0.6000,min_box_score=0.6000,text_det_box_thresh=0.6075,text_det_unclip_ratio=1._2025-12-06_22-26-05\n", "2025-12-06 22:32:18,370\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00031_31_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.4511,text_det_unclip_ratio=1.3534,_2025-12-06_22-32-18\n", "2025-12-06 22:32:18,374\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00031_31_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.4511,text_det_unclip_ratio=1.3534,_2025-12-06_22-32-18\n", "\u001b[36m(trainable_paddle_ocr pid=7328)\u001b[0m [2025-12-06 22:32:21,245 E 7328 10556] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:32:23,134\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00031_31_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.4511,text_det_unclip_ratio=1.3534,_2025-12-06_22-32-18\n", "2025-12-06 22:32:23,136\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00031_31_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.4511,text_det_unclip_ratio=1.3534,_2025-12-06_22-32-18\n", "\u001b[36m(trainable_paddle_ocr pid=11640)\u001b[0m [2025-12-06 22:32:53,354 E 11640 20276] core_worker_process.cc:837: Failed to establish connection to the metrics exporter agent. Metrics will not be exported. Exporter agent status: RpcError: Running out of retries to initialize the metrics agent. rpc_code: 14\n", "2025-12-06 22:37:58,564\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00030_30_line_tolerance=0.7000,min_box_score=0.6000,text_det_box_thresh=0.6545,text_det_unclip_ratio=1._2025-12-06_22-31-46\n", "2025-12-06 22:38:36,893\tWARNING trial.py:647 -- The path to the trial log directory is too long (max length: 260. Consider using `trial_dirname_creator` to shorten the path. Path: C:\\Users\\Sergio\\AppData\\Local\\Temp\\ray\\session_2025-12-06_20-08-28_976013_10020\\artifacts\\2025-12-06_20-56-49\\trainable_paddle_ocr_2025-12-06_20-56-49\\driver_artifacts\\trainable_paddle_ocr_b3bdc_00031_31_line_tolerance=0.5000,min_box_score=0,text_det_box_thresh=0.4511,text_det_unclip_ratio=1.3534,_2025-12-06_22-32-18\n", "2025-12-06 22:38:36,952\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to 'C:/Users/Sergio/ray_results/trainable_paddle_ocr_2025-12-06_20-56-49' in 0.0464s.\n", "2025-12-06 22:38:36,993\tINFO tune.py:1041 -- Total run time: 6107.63 seconds (6107.54 seconds for the tuning loop).\n" ] } ], "source": [ "from ray.tune.search.optuna import OptunaSearch\n", "\n", "def trainable_paddle_ocr(config):\n", " args = [sys.executable, SCRIPT_ABS, \"--pdf-folder\", PDF_FOLDER_ABS]\n", " for k, v in config.items():\n", " args += [f\"--{KEYMAP[k]}\", str(v)]\n", " proc = subprocess.run(args, capture_output=True, text=True, cwd=SCRIPT_DIR)\n", "\n", " if proc.returncode != 0:\n", " tune.report({\"CER\": 1.0, \"WER\": 1.0, \"TIME\": 0.0, \"ERROR\": proc.stderr[:500]})\n", " return\n", " # última línea = JSON con métricas\n", " last = proc.stdout.strip().splitlines()[-1]\n", " \n", " metrics = json.loads(last)\n", " tune.report(metrics=metrics)\n", "\n", "tuner = tune.Tuner(\n", " trainable_paddle_ocr,\n", " tune_config=tune.TuneConfig(metric=\"CER\", \n", " mode=\"min\", \n", " search_alg=OptunaSearch(),\n", " num_samples=128, \n", " max_concurrent_trials=4),\n", " run_config=air.RunConfig(verbose=2, log_to_file=False),\n", " param_space=search_space\n", ")\n", "\n", "results = tuner.fit()\n", "\n" ] }, { "cell_type": "code", "execution_count": 74, "id": "710a67ce", "metadata": {}, "outputs": [], "source": [ "df = results.get_dataframe()" ] }, { "cell_type": "code", "execution_count": 75, "id": "1ab345a3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Guardado: raytune_paddle_subproc_results_20251207_082539.csv\n",
       "
\n" ], "text/plain": [ "Guardado: raytune_paddle_subproc_results_20251207_082539.csv\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate a unique filename with timestamp\n", "timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n", "filename = f\"raytune_paddle_subproc_results_{timestamp}.csv\"\n", "filepath = os.path.join(OUTPUT_FOLDER, filename)\n", "\n", "\n", "df.to_csv(filename, index=False)\n", "print(f\"Guardado: {filename}\")" ] }, { "cell_type": "code", "execution_count": 76, "id": "3e3a34e4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CERWERTIMEPAGESTIME_PER_PAGEtimestamptraining_iterationtime_this_iter_stime_total_spidtime_since_restoreiterations_since_restoreconfig/text_det_box_threshconfig/text_det_unclip_ratioconfig/text_rec_score_threshconfig/line_toleranceconfig/min_box_score
count32.00000032.00000032.00000032.032.0000003.200000e+0132.032.00000032.00000032.00000032.00000032.032.00000032.00000032.00000032.00000032.000000
mean0.1152140.244518352.8984645.070.4769071.765054e+091.0375.418998375.41899813340.500000375.4189981.00.5685911.5681330.2250000.6218750.378125
std0.0477970.07196010.4631740.02.0887991.775117e+030.011.08785311.0878536600.34014811.0878530.00.0840850.2340270.1665590.0750670.262414
min0.0629820.148775331.4395325.066.1946261.765051e+091.0352.481225352.4812251072.000000352.4812251.00.4255311.2031750.0000000.5000000.000000
25%0.0672740.163547346.6481905.069.2309201.765053e+091.0368.239074368.2390749455.000000368.2390741.00.4971801.3463490.0000000.6000000.000000
50%0.1120580.243838352.2414855.070.3531911.765054e+091.0374.715855374.71585512292.000000374.7158551.00.5726091.6355900.2000000.6000000.500000
75%0.1422520.301922359.4839475.071.7873561.765056e+091.0381.121178381.12117819556.000000381.1211781.00.6499001.7881650.4000000.7000000.600000
max0.2124770.371172376.2772485.075.1484841.765057e+091.0399.524998399.52499823532.000000399.5249981.00.6987421.9285210.4000000.7000000.600000
\n", "
" ], "text/plain": [ " CER WER TIME PAGES TIME_PER_PAGE timestamp \\\n", "count 32.000000 32.000000 32.000000 32.0 32.000000 3.200000e+01 \n", "mean 0.115214 0.244518 352.898464 5.0 70.476907 1.765054e+09 \n", "std 0.047797 0.071960 10.463174 0.0 2.088799 1.775117e+03 \n", "min 0.062982 0.148775 331.439532 5.0 66.194626 1.765051e+09 \n", "25% 0.067274 0.163547 346.648190 5.0 69.230920 1.765053e+09 \n", "50% 0.112058 0.243838 352.241485 5.0 70.353191 1.765054e+09 \n", "75% 0.142252 0.301922 359.483947 5.0 71.787356 1.765056e+09 \n", "max 0.212477 0.371172 376.277248 5.0 75.148484 1.765057e+09 \n", "\n", " training_iteration time_this_iter_s time_total_s pid \\\n", "count 32.0 32.000000 32.000000 32.000000 \n", "mean 1.0 375.418998 375.418998 13340.500000 \n", "std 0.0 11.087853 11.087853 6600.340148 \n", "min 1.0 352.481225 352.481225 1072.000000 \n", "25% 1.0 368.239074 368.239074 9455.000000 \n", "50% 1.0 374.715855 374.715855 12292.000000 \n", "75% 1.0 381.121178 381.121178 19556.000000 \n", "max 1.0 399.524998 399.524998 23532.000000 \n", "\n", " time_since_restore iterations_since_restore \\\n", "count 32.000000 32.0 \n", "mean 375.418998 1.0 \n", "std 11.087853 0.0 \n", "min 352.481225 1.0 \n", "25% 368.239074 1.0 \n", "50% 374.715855 1.0 \n", "75% 381.121178 1.0 \n", "max 399.524998 1.0 \n", "\n", " config/text_det_box_thresh config/text_det_unclip_ratio \\\n", "count 32.000000 32.000000 \n", "mean 0.568591 1.568133 \n", "std 0.084085 0.234027 \n", "min 0.425531 1.203175 \n", "25% 0.497180 1.346349 \n", "50% 0.572609 1.635590 \n", "75% 0.649900 1.788165 \n", "max 0.698742 1.928521 \n", "\n", " config/text_rec_score_thresh config/line_tolerance \\\n", "count 32.000000 32.000000 \n", "mean 0.225000 0.621875 \n", "std 0.166559 0.075067 \n", "min 0.000000 0.500000 \n", "25% 0.000000 0.600000 \n", "50% 0.200000 0.600000 \n", "75% 0.400000 0.700000 \n", "max 0.400000 0.700000 \n", "\n", " config/min_box_score \n", "count 32.000000 \n", "mean 0.378125 \n", "std 0.262414 \n", "min 0.000000 \n", "25% 0.000000 \n", "50% 0.500000 \n", "75% 0.600000 \n", "max 0.600000 " ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 90, "id": "50fa5b59", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Correlación con CER:\n",
       " CER                             1.000000\n",
       "config/text_det_box_thresh      0.758837\n",
       "config/text_det_unclip_ratio    0.387201\n",
       "config/text_rec_score_thresh    0.193323\n",
       "config/line_tolerance           0.141715\n",
       "config/textline_orientation     0.035649\n",
       "config/min_box_score           -0.185718\n",
       "Name: CER, dtype: float64\n",
       "
\n" ], "text/plain": [ "Correlación con CER:\n", " CER \u001b[1;36m1.000000\u001b[0m\n", "config/text_det_box_thresh \u001b[1;36m0.758837\u001b[0m\n", "config/text_det_unclip_ratio \u001b[1;36m0.387201\u001b[0m\n", "config/text_rec_score_thresh \u001b[1;36m0.193323\u001b[0m\n", "config/line_tolerance \u001b[1;36m0.141715\u001b[0m\n", "config/textline_orientation \u001b[1;36m0.035649\u001b[0m\n", "config/min_box_score \u001b[1;36m-0.185718\u001b[0m\n", "Name: CER, dtype: float64\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Correlación con WER:\n",
       " WER                             1.000000\n",
       "config/text_det_unclip_ratio    0.804665\n",
       "config/text_det_box_thresh      0.394131\n",
       "config/text_rec_score_thresh    0.316860\n",
       "config/line_tolerance           0.032678\n",
       "config/textline_orientation    -0.187603\n",
       "config/min_box_score           -0.243325\n",
       "Name: WER, dtype: float64\n",
       "
\n" ], "text/plain": [ "Correlación con WER:\n", " WER \u001b[1;36m1.000000\u001b[0m\n", "config/text_det_unclip_ratio \u001b[1;36m0.804665\u001b[0m\n", "config/text_det_box_thresh \u001b[1;36m0.394131\u001b[0m\n", "config/text_rec_score_thresh \u001b[1;36m0.316860\u001b[0m\n", "config/line_tolerance \u001b[1;36m0.032678\u001b[0m\n", "config/textline_orientation \u001b[1;36m-0.187603\u001b[0m\n", "config/min_box_score \u001b[1;36m-0.243325\u001b[0m\n", "Name: WER, dtype: float64\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "param_cols = [\n", " \"config/text_det_box_thresh\",\n", " \"config/text_det_unclip_ratio\",\n", " \"config/text_rec_score_thresh\",\n", " \"config/line_tolerance\",\n", " \"config/min_box_score\",\n", " \"config/textline_orientation\"\n", "]\n", "# Correlación de Pearson con CER y WER\n", "corr_cer = df[param_cols + [\"CER\"]].corr()[\"CER\"].sort_values(ascending=False)\n", "corr_wer = df[param_cols + [\"WER\"]].corr()[\"WER\"].sort_values(ascending=False)\n", "\n", "print(\"Correlación con CER:\\n\", corr_cer)\n", "print(\"Correlación con WER:\\n\", corr_wer)" ] }, { "cell_type": "code", "execution_count": 91, "id": "9462b7a2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
textline_orientation=True:\n",
       "
\n" ], "text/plain": [ "\u001b[33mtextline_orientation\u001b[0m=\u001b[3;92mTrue\u001b[0m:\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             CER        WER\n",
       "count  14.000000  14.000000\n",
       "mean    0.117115   0.229452\n",
       "std     0.051623   0.064449\n",
       "min     0.063913   0.148775\n",
       "25%     0.065857   0.162757\n",
       "50%     0.111765   0.236537\n",
       "75%     0.152931   0.287554\n",
       "max     0.198069   0.331298\n",
       "
\n" ], "text/plain": [ " CER WER\n", "count \u001b[1;36m14.000000\u001b[0m \u001b[1;36m14.000000\u001b[0m\n", "mean \u001b[1;36m0.117115\u001b[0m \u001b[1;36m0.229452\u001b[0m\n", "std \u001b[1;36m0.051623\u001b[0m \u001b[1;36m0.064449\u001b[0m\n", "min \u001b[1;36m0.063913\u001b[0m \u001b[1;36m0.148775\u001b[0m\n", "\u001b[1;36m25\u001b[0m% \u001b[1;36m0.065857\u001b[0m \u001b[1;36m0.162757\u001b[0m\n", "\u001b[1;36m50\u001b[0m% \u001b[1;36m0.111765\u001b[0m \u001b[1;36m0.236537\u001b[0m\n", "\u001b[1;36m75\u001b[0m% \u001b[1;36m0.152931\u001b[0m \u001b[1;36m0.287554\u001b[0m\n", "max \u001b[1;36m0.198069\u001b[0m \u001b[1;36m0.331298\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n",
       "textline_orientation=False:\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[33mtextline_orientation\u001b[0m=\u001b[3;91mFalse\u001b[0m:\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             CER        WER\n",
       "count  18.000000  18.000000\n",
       "mean    0.113735   0.256237\n",
       "std     0.046073   0.077033\n",
       "min     0.062982   0.149490\n",
       "25%     0.075005   0.212741\n",
       "50%     0.112058   0.255814\n",
       "75%     0.135998   0.318958\n",
       "max     0.212477   0.371172\n",
       "
\n" ], "text/plain": [ " CER WER\n", "count \u001b[1;36m18.000000\u001b[0m \u001b[1;36m18.000000\u001b[0m\n", "mean \u001b[1;36m0.113735\u001b[0m \u001b[1;36m0.256237\u001b[0m\n", "std \u001b[1;36m0.046073\u001b[0m \u001b[1;36m0.077033\u001b[0m\n", "min \u001b[1;36m0.062982\u001b[0m \u001b[1;36m0.149490\u001b[0m\n", "\u001b[1;36m25\u001b[0m% \u001b[1;36m0.075005\u001b[0m \u001b[1;36m0.212741\u001b[0m\n", "\u001b[1;36m50\u001b[0m% \u001b[1;36m0.112058\u001b[0m \u001b[1;36m0.255814\u001b[0m\n", "\u001b[1;36m75\u001b[0m% \u001b[1;36m0.135998\u001b[0m \u001b[1;36m0.318958\u001b[0m\n", "max \u001b[1;36m0.212477\u001b[0m \u001b[1;36m0.371172\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Direct comparison for binary parameter\n", "print(\"textline_orientation=True:\")\n", "print(df[df[\"config/textline_orientation\"] == True][[\"CER\", \"WER\"]].describe())\n", "\n", "print(\"\\ntextline_orientation=False:\")\n", "print(df[df[\"config/textline_orientation\"] == False][[\"CER\", \"WER\"]].describe())\n", "\n", "# Or a simple mean comparison\n", "df.groupby(\"config/textline_orientation\")[[\"CER\", \"WER\"]].mean()\n", "\n", "import seaborn as sns\n", "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", "sns.boxplot(data=df, x=\"config/textline_orientation\", y=\"CER\", ax=axes[0])\n", "sns.boxplot(data=df, x=\"config/textline_orientation\", y=\"WER\", ax=axes[1])" ] }, { "cell_type": "markdown", "id": "bc78df46", "metadata": {}, "source": [ "## Interpretation:\n", "\n", "CER: Essentially identical — orientation detection doesn't help at the character level\n", "WER: True is meaningfully better (~2.7 percentage points lower mean, tighter distribution)\n", "\n", "This makes sense: orientation detection helps keep words intact by properly aligning text boxes, which reduces word-level errors even when individual characters are recognized correctly." ] }, { "cell_type": "code", "execution_count": 85, "id": "02fc0a87", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(df[\"config/text_det_box_thresh\"], df[\"CER\"])\n", "plt.xlabel(\"Detection Box Threshold\")\n", "plt.ylabel(\"CER\")\n", "plt.title(\"Effect of Detection Threshold on Character Error Rate\")\n", "plt.show()\n", "\n", "plt.scatter(df[\"config/textline_orientation\"], df[\"CER\"])\n", "plt.xlabel(\"Detection Box Threshold\")\n", "plt.ylabel(\"CER\")\n", "plt.title(\"Effect of Detection Threshold on Character Error Rate\")\n", "plt.show()\n", "\n", "plt.scatter(df[\"config/line_tolerance\"], df[\"CER\"])\n", "plt.xlabel(\"Line Tolerance\")\n", "plt.ylabel(\"WER\")\n", "plt.title(\"Effect of Line Tolerance on Character Error Rate\")\n", "plt.show()\n", "\n", "plt.scatter(df[\"config/text_det_unclip_ratio\"], df[\"CER\"])\n", "plt.xlabel(\"Detection Box Threshold\")\n", "plt.ylabel(\"CER\")\n", "plt.title(\"Effect of Text detection expansion coefficient on Character Error Rate\")\n", "plt.show()\n", "\n", "plt.scatter(df[\"config/text_rec_score_thresh\"], df[\"WER\"])\n", "plt.xlabel(\"Line Tolerance\")\n", "plt.ylabel(\"WER\")\n", "plt.title(\"Effect of Text recognition threshold on Character Error Rate\")\n", "plt.show()\n", "\n", "plt.scatter(df[\"config/text_rec_score_thresh\"], df[\"WER\"])\n", "plt.xlabel(\"Line Tolerance\")\n", "plt.ylabel(\"WER\")\n", "plt.title(\"Effect of Text recognition threshold on Character Error Rate\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 86, "id": "cc1e3d53", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", 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", 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dHCAFzQqKNE114ZQrFZwbVlnh+zXZFCCHPwjEc9kqOw8Ffm7gduWVV9r9qHqUqszuPpTrAU0Vrzt06GAefvhhG4gqEFbupOoexlKJuTrckwiA0oQuFCpaqOiJROl0gdYFp7xcoMoUh7Vq1cpG+jrgg090tdJyp8eT+5SmJ7ZYnsBURKRKvjoR1eJCwcm7774bcgOravFfMD3p6KKup6lY9oOWIby/k2DabiqWUDFfcC5Q+HbV/9r2amEX/FSrnLJ4SsVjIh77LRq34r8qh8dynCm9coH00dOvKq2raGHRokWV+l1tG1U+DlfRtnPHuzlXwSLNL9r3lTY4J0QBiI6tWLZBrHTTVU6ZchkU6CjHJ7gPGgVDixcvDjzIuAGQzjFVIo62fXSsVVSJOXg7Befu6VzUekbqpDFZ4rF+lTVhwgTbLcFPfvKTQHG6KjwfP37ctqoNzrV0i31jOQdjvSd5iTpAaUL1YZRV6eZ0BFOxjFoiiFqI6AlNLT3Ke3JTS4bwpq/R6AlOzYB1wXLp99TySvUEIvXseip0Q1KLCLWMUL2HcMpGdmkd3BZt6txRgZCKAvR3MK1vLOXaFV3UtX1Vv0ctucpbLqVTebjqJ0XbD25nZeFp9ASmi47b0sbNBQvvw0PbP57c/kViOS6SdUxUZpkqS9tVN2YdK7o5RtufKh5QcUL4xV9Bq24ilaVtt3LlypDeedW6R8WqaiUUrT6P6oko6FKLseBjWfVSotUbCqYblZ7yVSQSfC1wOy0sr1VhVSio0TZUc2vl+AQHygqAFAS8+OKLgZax7jl2+eWX2/HBXQyo5ZxaEmqeFRXhKpdDgcb8+fNtcOdSy9hEHEeVEY/1q6wGDRrYVlq6d6gOmbscEnwc6BjQvgoX7V4R6z3JS+QAeUQVWN0numA68StTDu1S02hF68rSdJvV6qL5wQcf2OapOplUZq1sTlUC1kVOT0BqxqqndDWD1zQ1xxR9X7kPyv5UPRSVM0eql+CWKSsY0e+qvF4Xaf2mnu7U1PFUOy+LVolcFwM9NaoSoLaZLhI64VTE9f7779t0am6tyr1aF53UWl8FRKpvoIrQ7tOe1lc369GjR9s+aHSTVpFGZU2fPt0+JWlbabl0s1Jum4IuLYP+FtUPUf8v+j3d7FRZUPtLaVSZWcum39c+0ROa9p+WVRUkdXFUkYubQ6FlV0Clba11dZvBq++UeOaS6Oaqbaj6LroYKmvf7SfEq2OiMstUWbrRqCmwzhd1HqfuCnTjVNN2VQhWZV0Fp9rOKi7QBV/7WzmL6ntHx6O+U1kqjnjmmWdsgKtK48qpVVCj3AkF19GKU0TFbgpUdG6oLyodbwo6VWdET+Pl0bqpibIejnSeqPmzghAF1jon4t0JqZuro3M2uK8qcbuSUNccOg+Cj2Gduwrq9H2dK9reOtYUbMbSD5FyXDUP3fR1rKh+kbatbu6VufaqoUWk3D1dO06lB/1TXb+q+NGPfmTPS12/nn32WRuEKRjWttd20rGjXCKdV+EPnbr+6DzRcivHTmm0XWO9J3nK62ZoflNeM/jwJq2VaQbvNpUdP3680759e9uMskmTJrZZ+MyZMwPNTUVNJtVkt0OHDjbdGWec4QwcONA2p3Zt3LjRNrlUU1X9XkVN4nfv3u2MHDnS/qbmqSbqwetS0TpVthm8qJnosGHDbNPb0047zWnRooVz5ZVXOs8//3xIU+qf//znId9TE2wtR+fOnQPb5ciRI85NN91kmz3H0mQ42j5wt4WmtWzZ0i6Xlk/N9tXMNryZ6YQJE2zzXzedmrgHN3/VPr377rud5s2b2zRq4q99F9xlgRw9etT+ZqNGjWwzczWl3bRpk13O6dOnn9QMXts00nGpprLRmsHLggULbDNYNWMNbn4e3gw+1mPCbZobqQl5pH0eSbRlinashS+ru+7vvfdexPlrfmr+q6bv2dnZTrt27WwXA//85z/tdHV5oG2v80nNfpWuV69eznPPPRcyn1iXR3QM6FjQ8ajf7Nmzp/OnP/0pJE2kZvDyhz/8wfnqV79qm9fn5+c7S5Yssfsx1m4e1Oxd66LjrVmzZs6oUaNsU//wZVbz53CV+R0dszVq1LDroKb64Tp16mSnPfjggydNW716td0nOtbVxcVll13mvP322yFpKtqvam6vc0/bqXv37rZ7gkj7orLN4IPXX9tDx0RlryHxWL9w5Z1romNa55DbvcZLL71k94GOP3W/oP3gdt8RfJ1Qc3od12qmH96NQKz3JK9k6B9vQzAAiaDsbFXA1VOq2/ssAOC/qAMEpAH1qRJOWdoqLlGPyQCAUNQBAtKA6gaoro3qDKnOgOqY6aO6OPFuNQIA6YAiMCANqNKkKq+qtY8qLKrpqirvqgJ1eZ0tAoBfEQABAADfoQ4QAADwHQIgAADgO1QOiEAdA3766ae2s7ZEdrUPAADiRz376PVB6sC3vE5DhQAoAgU/tJwBAKB62rFjhznzzDPLTUMAFIHbTb82YLzfuwIAABLj0KFDNgMjltftEABF4BZ7KfghAAIAoHqJpfoKlaABAIDvEAABAADfIQACAAC+QwAEAAB8hwAIAAD4DgEQAADwHQIgAADgOwRAAADAdwiAAACA79ATNADfKC1zzMptB8yewyWmab1s07NNI5OVyQuPAT8iAALgC4Uf7jJTXl5vdhWXBMbl5WSbSVflmwHn5Xm6bACSjyIwAL4IfkYtWh0S/EhRcYkdr+kA/IUACEDaF3sp58eJMM0dp+lKB8A/CIAApDXV+QnP+QmmsEfTlQ6AfxAAAUhrqvAcz3QA0gMBEIC0ptZe8UwHID0QAAFIa2rqrtZe0Rq7a7ymKx0A/yAAApDW1M+PmrpLeBDkDms6/QEB/kIABCDtqZ+feTd3Nbk5ocVcGtZ4+gEC/IeOEAH4goKcfvm59AQNwCIAAuAbKuYqaNfY68UAkAIoAgMAAL5DAAQAAHyHAAgAAPgOARAAAPAdAiAAAOA7BEAAAMB3CIAAAIDvEAABAADfIQACAAC+QwAEAAB8h1dhAACApCgtc1LmfXwEQAAAIOEKP9xlpry83uwqLgmMy8vJNpOuyrcvK042isAAAEDCg59Ri1aHBD9SVFxix2t6shEAAfBV9vuKrfvNi2s+sf9rGEBi6TxTzk+ks80dp+nJPh8pAgPgC6mW/Q74xcptB07K+QmmsEfTla6gXeOkLRc5QADSXipmvwN+sedwSVzTxQsBEIC0lqrZ74BfNK2XHdd08UIABCCtVSb7HUD8qam7ipujNXbXeE1XumQiAAKQ1lI1+x3wi6zMDFvXTsKDIHdY05PdHxABEIC0lqrZ74CfDDgvz8y7uavJzQk9zzSs8V40RKAVGABfZL+rwnOkWj4Z/7sIJzv7HfCbAeflmX75uSnTE3RK5ADNmTPHtG7d2mRnZ5tevXqZlStXRk27ZMkS0717d9OgQQNTt25d06VLF/P000+HpBkxYoTJyMgI+QwYMCAJawIg1aRq9jvgR1mZGbap+zVdWtj/vTzvPA+AFi9ebEaPHm0mTZpkVq9ebTp37mz69+9v9uzZEzF9o0aNzIQJE8yKFSvM2rVrzciRI+3nr3/9a0g6BTy7du0KfJ555pkkrRGAVJOK2e8AvJXhOI6nbT+V49OjRw8ze/ZsO1xWVmZatmxp7rzzTjNu3LiY5tG1a1dzxRVXmKlTpwZygA4ePGheeOGFKi3ToUOHTE5OjikuLjb169ev0jwApJ5UehEjgPirzP3b0xygEydOmFWrVpm+ffv+3wJlZtph5fBURLHbsmXLzKZNm8zFF18cMm358uWmadOm5pxzzjGjRo0y+/fvjzqf48eP240W/AGQflIp+x2AtzytBL1v3z5TWlpqmjVrFjJewxs3boz6PUV2LVq0sIFLVlaWmTt3runXr19I8dd1111n2rRpY7Zu3Wruu+8+M3DgQBtUKX24adOmmSlTpsR57QAAQKqqlq3A6tWrZ9asWWOOHDlic4BUh6ht27bm0ksvtdOHDBkSSNuxY0fTqVMn065dO5sr1KdPn5PmN378eDsPl3KAVAwHAADSk6cBUJMmTWyOzO7du0PGazg3Nzfq91RM1r59e/u3WoFt2LDB5uK4AVA4BUf6rS1btkQMgGrVqmU/AADAHzytA1SzZk3TrVs3m4vjUiVoDRcUFMQ8H31HxWHR7Ny509YBysujpQcAAEiBIjAVPQ0fPtz27dOzZ08za9Ysc/ToUdu0XYYNG2br+yiHR/S/0qpIS0HPK6+8YvsBmjdvnp2uYjHV57n++uttLpLqAN1zzz02x0jN6wEAADwPgAYPHmz27t1rJk6caIqKimyRVmFhYaBi9Pbt222Rl0vB0R133GFzdWrXrm06dOhgFi1aZOcjKlJT/0BPPvmkbQrfvHlzc/nll9sm8hRzAQCAlOgHKBXRDxAAAOl9//Y8BwgAAPhDaQp1RkoABAAAEq7ww11mysvrza7iksA4vahY7+Lz4nU0nr8LDAAApH/wM2rR6pDgR4qKS+x4TU82AiAAAJDQYi/l/ESqcOyO03SlSyYCIAAAkDCq8xOe8xNMYY+mK10yEQABAICEUYXneKaLFwIgAACQMGrtFc908UIABAAAEkZN3dXaK1pjd43XdKVLJgIgAACQMOrnR03dJTwIcoc1Pdn9AREAAQCAhFI/P/Nu7mpyc0KLuTSs8V70A0RHiAAAIOEU5PTLz6UnaAAA4C9ZmRmmoF1jkwooAgMAAL5DAAQAAHyHAAgAAPgOARAAAPAdWoEBAICk0AtP9c4vvfZCPT+r88Nk9//jIgACAAAJV/jhLvvW9+AXo6oHaHWC6EU/QBSBAQCAhAc/oxatPumt8EXFJXa8picbARAAAEhosZdyfpwI09xxmq50yUQABAAAEkZ1fsJzfoIp7NF0pUsmAiAAAJAwqvAcz3TxQgAEAAASRq294pkuXgiAAABAwqipu1p7RWvsrvGarnTJRAAEAAASRv38qKm7hAdB7rCmJ7s/IAIgAACQUOrnZ97NXU1uTmgxl4Y13ot+gOgIEQAAJJyCnH75ufQEDQAA/CUrM8MUtGtsUgFFYAAAwHcIgAAAgO9QBwiAb6TSm6gBeIsACIAvpNqbqAF4iyIwAGkvFd9EDcBbBEAA0lqqvokagLcIgACktVR9EzUAbxEAAUhrqfomagDeIgACkNZS9U3UALxFAAQgraXqm6gBeIsACEBaS9U3UQPwFgEQgLSXim+iBuAtOkIE4Aup9iZqAN4iAALgG6n0JmoA3qIIDAAA+A4BEAAA8B0CIAAA4DsEQAAAwHcIgAAAgO8QAAEAAN8hAAIAAL5DAAQAAHyHjhABAEBSlJY5KdMbOwEQAABIuMIPd5kpL683u4pLAuPycrLty4i9eB8fRWAAACDhwc+oRatDgh8pKi6x4zU92QiAAABAQou9lPPjRJjmjtN0pUsmAiAAAJAwqvMTnvMTTGGPpitdMhEAAQCAhFGF53imixcCIAAAkDBq7RXPdPFCKzCfNv8DACAZdK9Tay9VeI5Uy0d3wdyc/94Tk4kAyKfN/wAASAY96Otep9ZeCnaCgyA3C0DTk50hQBGYT5v/AQCQLHrQn3dzV5vTE0zDGu9FRgA5QB43/1O8q+n98nMpDgMApK0B5+XZe12qVAUhAEqh5n8F7RonenEAAPCMgp1UudcRAPm0+R8AAH5uDEQA5NPmfwAA+LkxUEpUgp4zZ45p3bq1yc7ONr169TIrV66MmnbJkiWme/fupkGDBqZu3bqmS5cu5umnnw5J4ziOmThxosnLyzO1a9c2ffv2NZs3bzZeNv+LFt9qfJ4Hzf8AAPBzYyDPA6DFixeb0aNHm0mTJpnVq1ebzp07m/79+5s9e/ZETN+oUSMzYcIEs2LFCrN27VozcuRI+/nrX/8aSDNjxgzz6KOPmvnz55t3333XBkqaZ0lJiWfN/yQ8CPKy+R8AAH5+F1iGo+wSDynHp0ePHmb27Nl2uKyszLRs2dLceeedZty4cTHNo2vXruaKK64wU6dOtbk/zZs3N2PGjDFjx46104uLi02zZs3MwoULzZAhQyqc36FDh0xOTo79Xv369U06Zv0BAJAMK7buN0MXvFNhumdu/dopV5CuzP3b0zpAJ06cMKtWrTLjx48PjMvMzLRFVsrhqYiCnddee81s2rTJPPjgg3bctm3bTFFRkZ2HSxtDgZbmGSkAOn78uP0Eb8B0b/4HAICfGwN5GgDt27fPlJaW2tyZYBreuHFj1O8psmvRooUNWrKysszcuXNNv3797DQFP+48wufpTgs3bdo0M2XKFOOn5n8AAPi5MZDndYCqol69embNmjXmvffeM//v//0/W4do+fLlVZ6fcqAUVLmfHTt2xHV5AQDwq54p2hjI0xygJk2a2Byc3bt3h4zXcG5ubtTvqZisffv29m+1AtuwYYPNxbn00ksD39M81AoseJ5KG0mtWrXsBwAAxBfvAougZs2aplu3bmbZsmWBcaoEreGCgoKYN66+49bhadOmjQ2CguepOj1qDVaZeQIAgPjgXWARqPhq+PDhtm+fnj17mlmzZpmjR4/apu0ybNgwW99HOTyi/5W2Xbt2Nuh55ZVXbD9A8+bNs9MzMjLMXXfdZR544AFz1lln2YDo/vvvty3DBg0aFKddCQAAqnNjIM97gh48eLDZu3ev7bhQlZRVTFVYWBioxLx9+3Zb5OVScHTHHXeYnTt32k4OO3ToYBYtWmTn47rnnntsuttuu80cPHjQXHTRRXae6mgRAAB4I5UaA3neD1AqSkQ/QAAAILGqTT9AfpNKL4EDAMDPCICShJ6gAQBIHdWyH6DqJhVfAgcAgJ8RAPn0JXAAAPgZAVCCqc5PeM5PMIU9mq50AACks9Iyx74c9cU1n9j/vXz4pw6QT18CBwCAn+vCkgPk05fAAQDg57qwBEA+fQkcAAB+rgtLAJSkl8BJeBDkDms6/QEBANLRyhStC0sA5NOXwAEA4Oe6sFSC9ulL4AAA8HNdWAIgn74EDgCAZNaFVYXnSLV8Mv5XIpLsurAUgQEAAN/VhSUAAgAAvqsLSxEYAADwXV1YAiAAAOC7urAUgQEAAN8hAAIAAL5DERgA31BX+6lS/wCAtwiAAPhCqr2JGoC3KAIDkPZS8U3UALxFAATA12+idjx6EzUAbxEAAfD1m6iNR2+iBuAtAiAAaa3oUElc0wFIDwRAANLagSPH45oOQHogAAKQ1hrVrRnXdADSAwEQgLSWm1M7rukApAcCIABpTZ0dqr+f8mi60gHwDwIgAGlNPT2rs8No/T1rvKbTIzTgLwRAANKeenqed3PXk3KCNKzx9AQN+A+vwgDgCwpy+uXn8i4wABYBEADfUDFXQbvGXi8GgBRAERgAAPAdAiAAAOA7BEAAAMB3CIAAAIDvEAABAADfIQACAAC+QwAEAAB8hwAIAAD4TtwCoJKSEjNz5sx4zQ4AACA1AqC9e/eaP/3pT2bp0qWmtLTUjvviiy/ML37xC9O6dWszffr0RC0nAABA8l+F8eabb5orr7zSHDp0yGRkZJju3bub3/zmN2bQoEGmRo0aZvLkyWb48OHxWzIAAACvc4B+8pOfmG984xtm7dq1ZvTo0ea9994z1157rfnZz35m1q9fb773ve+Z2rVrJ2o5AQAA4ibDcRwnloSNGzc2b7zxhsnPzzeff/65Of30082SJUvMNddcY9KNcrlycnJMcXGxqV+/vteLAwAA4nz/jjkH6LPPPjNNmjSxfyunp06dOua8886L9esAAADVrw6QqKirqKjI/q2Mo02bNpmjR4+GpOnUqVN8lxAAAKSF0jLHrNx2wOw5XGKa1ss2Pds0MlmZGaldBJaZmWkrP0dK7o7X/27rsOqMIjAAAOKr8MNdZsrL682u4pLAuLycbDPpqnwz4Ly8pN+/Y84B2rZtWzyWDQAA+DD4GbVotQnPQikqLrHj593cNW5BUKxiDoBatWqV2CUBAABpWew15eX1JwU/onEqANP0fvm5SS0Oi7kS9IwZM2zrL9dbb71ljh8/Hhg+fPiwueOOO+K/hAAAoNpaue1ASLFXpCBI05UumWIOgMaPH2+DHNfAgQPNJ598Ehg+duyY+dWvfhX/JQQAANXWnsMlcU2X9AAovPJzjHWnAQCAjzWtlx3XdPHC2+ABAEDCqKm7WntFq92j8ZqudMlEAAQAABJGFZvV1F3CgyB3WNOT3R9QpTpCfPzxx+0rMOTLL780CxcuDPQOHVw/CAAAwKUm7mrqHt4PUG6c+wFKSEeIrVu3th0d+qG/IDpCBACg+vUEnZCOEF9//XXTpk2beCwfAADwoazMDFPQrrFJBTHXAWrXrp0NgL7zne+YRYsWhTSBBwAAqE5izgF67bXXzPLly+3nmWeeMSdOnDBt27Y1X//6181ll11mP82aNUvs0gIAACSzDlCwkpIS8/bbbwcCopUrV5ovvvjCdOjQwaxbt85Ud9QBAgAgve/fVQqAXMoF0isx/vKXv9heoI8cOcLb4AH4tgImAG8lpBK0G/C88847tkK0cn7effdd07JlS3PxxReb2bNnm0suueRUlx0AEvY26vAmuHkeNsEF4K2Yc4BU10cBjypCK9Dp3bu3/T8vL/0uHBSBAekX/IxatPqkt1G7eT/qn4QgCPDX/TvmVmBvvPGGady4sQ2E+vTpY/r165eWwQ+A9Cv2Us5PpCc9d5ymKx2AxNJ5tmLrfvPimk/s/16edzEHQAcPHjSPPfaYqVOnjnnwwQdN8+bNTceOHc0PfvAD8/zzz5u9e/dWeSHmzJljO1rMzs42vXr1spWqo1mwYIHNfWrYsKH99O3b96T0I0aMsJ02Bn8GDBhQ5eUDUH2pzk9wsVc4XX41XekAJDYn9qIHXzNDF7xjfvTsGvu/hjU+pQOgunXr2iBi+vTptihs3759ZsaMGTYg0v9nnnmmOe+88yq9AIsXLzajR482kyZNMqtXrzadO3c2/fv3N3v27ImYXnWPhg4daushrVixwtZBuvzyy0/ql0jLumvXrsBHTfcB+I8qPMczHYCqF0OHP4wUFZfY8V4EQVV+GaoCokaNGtmPcmJq1KhhNmzYUOn5PPzww+bWW281I0eONPn5+Wb+/Pk2qHriiScipv/tb39r7rjjDtOlSxfb7F7vJysrKzPLli0LSVerVi2Tm5sb+GgZAfiPWnvFMx2A9CiGjjkAUpChoibl9gwcONA0aNDAXHDBBWbu3Lk2wFAx1kcffWQq26ps1apVthgrsECZmXZYuTuxOHbsmO2DSIFYeE5R06ZNzTnnnGNGjRpl9u/fH3Uex48ftxWngj8A0oOauqu1V7TG7hqv6UoHwD/F0DE3g1fAc/ToURvsqNfnRx55xFx66aX2FRlVpWK00tLSk3qQ1vDGjRtjmse9995r6yMFB1Eq/rruuutsi7WtW7ea++67zwZtCqqysrJOmse0adPMlClTqrweAFKX+vlRU3dlsyvYCX7GdIMiTac/IMBfxdAxB0APPfSQDXzOPvtskypUH+nZZ5+1uT2qQO0aMmRI4G9V1O7UqZMN1JROLdjCjR8/3tZDcikHSHWLAKQHNXFXU/fwfoBy6QcI8G0xdMwB0O233x73H2/SpInNkdm9e3fIeA0rp6k8M2fOtAHQq6++agOc8uidZfqtLVu2RAyAVF9IHwDpHQT1y8+lJ2jAo2LoouKSiPWAMv73MJLsYugqV4KOh5o1a5pu3bqFVGB2KzQXFBRE/Z7qIU2dOtUUFhaa7t27V/g7O3futHWA6LcI8DcVcxW0a2yu6dLC/k+xF5C8YmgJr4vnZTG0pwGQqOhJffs8+eSTthWZKiyrrpFahcmwYcNsEZVLfRDdf//9tpWY+g4qKiqyH72HTPT/j3/8Y/vKjo8//tgGU9dcc41p3769bV4PAAC8KYZWTk8wDXvVE3ul3gWWCIMHD7adKE6cONEGMmrerpwdt2L09u3bbcsw17x582zrsRtuuCFkPupHaPLkybZIbe3atTagUueNqiCtfoKUY0QxFwAA3ki1YuhTeht8uuJdYAAAVD8JeRcYAABAuiAAAgAAvkMABAAAfIcACAAA+A4BEAAA8B0CIAAA4Due9wMEAAD8obTMSZl+gAiAAABAwhV+uOukFxLrHWF6DYYXPUFTBAYAABIe/IxatDok+BG9IFXjNT3ZCIAAAEBCi72U8xPptRPuOE1XumQiAAIAAAmjOj/hOT/BFPZoutIlEwEQAABIGFV4jme6eCEAAgAACaPWXvFMFy8EQAAAIGG6tWpoKmrprulKl0wEQAAAIGFW/eczU1H9Zk1XumQiAAIAAAlDHSAAAOA7TakDBAAA/KZnm0a2x+do1YA0XtOVLpkoAgMAAAmjd33pdRcSHgS5w5qe7HeCEQABAICE0ru+5t3c1eTmhDZ117DGe/EuMF6GCgAAEk5BTr/8XN4GDwAA/CUrM8MUtGtsUgFFYAAAwHcIgAAAgO8QAAEAAN8hAAIAAL5DAAQAAHyHAAgAAPgOARAAAPAdAiAAAOA7BEAAAMB3CIAAAIDvEAABAADfIQACAAC+QwAEAAB8hwAIAAD4Tg2vF8BPSsscs3LbAbPncIlpWi/b9GzTyGRlZni9WAAA+A4BUJIUfrjLTHl5vdlVXBIYl5eTbSZdlW8GnJeXrMUAAAAUgSUv+Bm1aHVI8CNFxSV2vKYDAIDkoQ5QEoq9lPPjRJjmjtN0pQMAAMlBAJRgqvMTnvMTTGGPpisdAABIDgKgBFOF53imAwAAp44AKMHU2iue6QAAwKkjAEowNXVvUOe0ctNoutIBAIDkIABKghNflpU7/YsKpgMAgPgiAEqwd7buN8dOlJab5uiJUpsOQGKpteWKrfvNi2s+sf/T+hLwLzpCTLAVH+2LOd2FZzVJ9OIAvkVnpACCkQOUcLG+6oJXYgCJQmekAMIRACVYQbvGcU0HoHLojBRAJARACfa1to0rbAXWsM5pNh2A+KMzUgCREAAlmN72Pv26juWmmXZdR94KDyQInZECiIQAKAn0tvf5N3c1ufVDOzvU2+A1nrfBA4lDZ6QAIqEVWJIoyOmXn2uz4/VEqouyOj9UDhGAxNF5poeNouKSiC8l1hmYm/Pf8xGAfxAAJZGCHSo7A8k/7yZdlW9GLVptg53gIMh9/NB0HkYAf6EIDIAvcmDnqRg6J7QYWsMaTzE04D/kAAHwBYqhgdTolmJlilQFIQAC4BsUQwPeKfxwl5ny8nqzq7gkME7181QE7UUuLEVgAADAd72xEwABAADf9cZOAAQAAHzXGzsBEAAA8F1v7ARAAADAd72xEwABAICE98YerbG7xud50Bs7ARAAAEh4b+wSHgR52Rt7SgRAc+bMMa1btzbZ2dmmV69eZuXKlVHTLliwwPTu3ds0bNjQfvr27XtSesdxzMSJE01eXp6pXbu2TbN58+YkrAkAAKgOvbF73hHi4sWLzejRo838+fNt8DNr1izTv39/s2nTJtO0adOT0i9fvtwMHTrUXHDBBTZgevDBB83ll19u1q1bZ1q0aGHTzJgxwzz66KPmySefNG3atDH333+/nef69evtdwAAgL97Y89wlF3iIQU9PXr0MLNnz7bDZWVlpmXLlubOO+8048aNq/D7paWlNidI3x82bJjN/WnevLkZM2aMGTt2rE1TXFxsmjVrZhYuXGiGDBlS4TwPHTpkcnJy7Pfq168fh7UEAACJVpn7t6dFYCdOnDCrVq2yRVSBBcrMtMMrVqyIaR7Hjh0zX3zxhWnU6L+Vp7Zt22aKiopC5qmNoUAr1nkCAID05mkR2L59+2wOjnJngml448aNMc3j3nvvtTk+bsCj4MedR/g83Wnhjh8/bj/BESQAAEhfKVEJuqqmT59unn32WfPHP/7xlOr2TJs2zeYSuR8VwQEAgPTlaQDUpEkTk5WVZXbv3h0yXsO5ubnlfnfmzJk2AFq6dKnp1KlTYLz7vcrMc/z48ba80P3s2LHjFNYKAACkOk8DoJo1a5pu3bqZZcuWBcapErSGCwoKon5PrbymTp1qCgsLTffu3UOmqdWXAp3geapI69133406z1q1atnKUsEfAACQvjxvBq8m8MOHD7eBTM+ePW0z+KNHj5qRI0fa6WrZpebtKqYSNXtXHz+/+93vbN9Bbr2e008/3X4yMjLMXXfdZR544AFz1llnBZrBq57QoEGDPF1XAACQGjwPgAYPHmz27t1rgxoFM126dLE5O24l5u3bt9uWYa558+bZ1mM33HBDyHwmTZpkJk+ebP++5557bBB12223mYMHD5qLLrrIzpM+gAAAQEr0A5SK6AcIAIDqp9r0AwQAAOAFAiAAAOA7BEAAAMB3CIAAAIDvEAABAADfIQACAAC+QwAEAAB8hwAIAAD4DgEQAADwHQIgAADgOwRAAADAdwiAAACA7xAAAQAA3yEAAgAAvkMABAAAfIcACAAA+A4BEAAA8B0CIAAA4DsEQAAAwHcIgAAAgO8QAAEAAN8hAAIAAL5DAAQAAHyHAAgAAPgOARAAAPAdAiAAAOA7BEAAAMB3CIAAAIDvEAABAADfIQACAAC+QwAEAAB8hwAIAAD4DgEQAADwHQIgAADgOwRAAADAdwiAAACA7xAAAQAA3yEAAgAAvkMABAAAfIcACAAA+A4BEAAA8B0CIAAA4DsEQAAAwHdqeL0AflJa5piV2w6YPYdLTNN62aZnm0YmKzPD68UCAMB3CICSpPDDXWbKy+vNruKSwLi8nGwz6ap8M+C8vGQtBgAAoAgsecHPqEWrQ4IfKSouseM1HQAAP5SErNi637y45hP7v4a9Qg5QgmnnKucn0i7WOBWAaXq//FyKwwAAaaswxUpCqASdYKrzE57zEx4EabrSAQCQjgpTsCSEACjBVOE5nukAAEinkhDR9GQXhxEAJZhae8UzHQAA1cnKFC0JIQBKMDV1VxlntMbuGq/pSgcAQLrZk6IlIQRACaZ+flTBS8KDIHdY0+kPCACQjpqmaEkIAVASqHb7vJu7mtyc0J2rYY2nHyAAQLrqmaIlITSDTxIFOWrqTk/QAAA/loSMWrTaBjtOipSEZDiO410vRCnq0KFDJicnxxQXF5v69et7vTgAAFR7hUnoB6gy929ygAAAgO9KQgiAAPgGLyQGvJWVmWEK2jVOid1AAATAF1KtG34A3qIVmE9fAgf4SSp2ww/AW+QAJQlPn4A3eCExgEjIAfLw6VPDPH0C/uyGH4C3CIA8fPoUx6OXwAF+kard8APwFgGQx0+fwtMn4L9u+AF4iwAowYqKP49rOgBV64a/PLyQGPAfAqAEO3D0RFzTAah8vyNXdy6/mbum80JiwF88D4DmzJljWrdubbKzs02vXr3MypUro6Zdt26duf766236jIwMM2vWrJPSTJ482U4L/nTo0MF4pdHpteKaDkDlqH7dS++X38xd06mHB/irOxhPm8EvXrzYjB492syfP98GPwpo+vfvbzZt2mSaNm16Uvpjx46Ztm3bmhtvvNHcfffdUed77rnnmldffTUwXKOGd6vZtF6tuKYDkLh6eKnSQy2QjgpTrDNST3OAHn74YXPrrbeakSNHmvz8fBsI1alTxzzxxBMR0/fo0cM89NBDZsiQIaZWregBgwKe3NzcwKdJkybGM7EGtzQCAxKCVmCA9wpTsDNSzwKgEydOmFWrVpm+ffv+38JkZtrhFStWnNK8N2/ebJo3b25zi771rW+Z7du3l5v++PHj9g2ywZ942Xf0eFzTAagcWoEBqd0ZqfGoOxjPAqB9+/aZ0tJS06xZs5DxGi4qKqryfFWUtnDhQlNYWGjmzZtntm3bZnr37m0OHz4c9TvTpk0zOTk5gU/Lli1NvHDxBbzVrVVDU9HLpjVd6QD4pzNSzytBx9vAgQNtHaFOnTrZ+kSvvPKKOXjwoHnuueeifmf8+PGmuLg48NmxY0fcm+BGu/5qPE1wgcRZ9Z/PTEUPlpqudAD8UwztWQCkejlZWVlm9+7dIeM1rHo78dKgQQNz9tlnmy1btkRNo/pE9evXD/nEi5rWqoKXhAdB7rCm0wQX8NfFF/CLpinaGalnAVDNmjVNt27dzLJlywLjysrK7HBBQUHcfufIkSNm69atJi8v+TXMXardPu/mriY3rDM2DWu8F7XfAb9I1Ysv4Bc9U7QkxNNm8GoCP3z4cNO9e3fTs2dP2wz+6NGjtlWYDBs2zLRo0cLW0XErTq9fvz7w9yeffGLWrFljTj/9dNO+fXs7fuzYseaqq64yrVq1Mp9++qmZNGmSzWkaOnSoh2v63yCoX36uLePUk6YuttrZ5PwAybn4qrWJE+Xim+vBxRfwi6z/lYSotZfONydFSkI8DYAGDx5s9u7dayZOnGgrPnfp0sVWXnYrRqv1llqGuRTQnH/++YHhmTNn2s8ll1xili9fbsft3LnTBjv79+83Z5xxhrnooovMO++8Y//2mnYu/YwAyT/vUvHiC/jJgP+VhIT3A5TrYT9AGY7j0ANNGDWDV2swVYiOZ30gAN5JtU7YAD8qLXMSWhJSmfs3AdApbkAA1UeiL74Aqs/929MiMABIJoqhAaRtP0AAAAAVIQACAAC+QwAEAAB8hwAIAAD4DgEQAADwHQIgAADgOwRAAADAdwiAAACA7xAAAQAA36En6Ajc16OpS20AAFA9uPftWF5zSgAUweHDh+3/LVu2jPe+AQAASbiP651g5eFlqBGUlZWZTz/91NSrV89kZGTEPTpVYLVjx460fNEq61f9sQ+rt3Tff35YR9av6pTzo+CnefPmJjOz/Fo+5ABFoI125plnmkTSSZuOJ66L9av+2IfVW7rvPz+sI+tXNRXl/LioBA0AAHyHAAgAAPgOAVCS1apVy0yaNMn+n45Yv+qPfVi9pfv+88M6sn7JQSVoAADgO+QAAQAA3yEAAgAAvkMABAAAfIcACAAA+A4B0CmaM2eOad26tcnOzja9evUyK1euLDf973//e9OhQwebvmPHjuaVV145qRfLiRMnmry8PFO7dm3Tt29fs3nzZpMu6zdixAjbu3bwZ8CAAcZLlVnHdevWmeuvv96m17LPmjXrlOdZ3dZv8uTJJ+1D7XMvVWYdFyxYYHr37m0aNmxoPzrHwtNX5/MwlvVLtfOwMuu3ZMkS0717d9OgQQNTt25d06VLF/P000+n9P5LxDpW530Y7Nlnn7XLPmjQoOTvQwdV9uyzzzo1a9Z0nnjiCWfdunXOrbfe6jRo0MDZvXt3xPRvvfWWk5WV5cyYMcNZv36985Of/MQ57bTTnA8++CCQZvr06U5OTo7zwgsvOO+//75z9dVXO23atHE+//zztFi/4cOHOwMGDHB27doV+Bw4cMDxSmXXceXKlc7YsWOdZ555xsnNzXUeeeSRU55ndVu/SZMmOeeee27IPty7d6/jlcqu40033eTMmTPH+de//uVs2LDBGTFihD3ndu7cmRbnYSzrl0rnYWXX7/XXX3eWLFlirzFbtmxxZs2aZa87hYWFKbn/ErWO1XkfurZt2+a0aNHC6d27t3PNNdc4wZKxDwmATkHPnj2d73//+4Hh0tJSp3nz5s60adMipv/mN7/pXHHFFSHjevXq5dx+++3277KyMnvTeeihhwLTDx486NSqVcvekKr7+rknbfiB7qXKrmOwVq1aRQwQTmWe1WH9FAB17tzZSRWnur2//PJLp169es6TTz6ZFudhReuXaudhPM6X888/3z5wpeL+S8Q6psM+/PLLL50LLrjAefzxx09al2TtQ4rAqujEiRNm1apVNlsu+B1iGl6xYkXE72h8cHrp379/IP22bdtMUVFRSBq900TZidHmWZ3Wz7V8+XLTtGlTc84555hRo0aZ/fv3Gy9UZR29mGdVJXJZlBWtlw22bdvWfOtb3zLbt283XojHOh47dsx88cUXplGjRmlxHla0fql0Hp7q+ukhftmyZWbTpk3m4osvTrn9l6h1TId9+NOf/tQu+y233HLStGTtQ16GWkX79u0zpaWlplmzZiHjNbxx48aI39EOjZRe493p7rhoaarz+onKqK+77jrTpk0bs3XrVnPfffeZgQMH2oM6KyvLpPo6ejHPqkrUsugitHDhQnvR3bVrl5kyZYqtc/Lhhx+aevXqmeq2jvfee68N5tyLbXU/Dytav1Q6D6u6fsXFxaZFixbm+PHjdnnnzp1r+vXrl3L7L1HrWN334Ztvvml+/etfmzVr1kScnqx9SACEpBoyZEjgb1WS7tSpk2nXrp19kunTpw97oxrQRdal/aeAqFWrVua5556L+DSXyqZPn24rYer4U+XNdBNt/ar7eahAWzfPI0eO2NyR0aNH29zISy+91KSLitaxuu7Dw4cPm29/+9u2sn6TJk08XRaKwKpIO05R9u7du0PGazg3NzfidzS+vPTu/5WZZ3Vav0h0Quu3tmzZYpKtKuvoxTyrKlnLopYqZ599drXbhzNnzrQBwtKlS+3Nw1Xdz8OK1i+VzsOqrp+KWNq3b29bR40ZM8bccMMNZtq0aSm3/xK1jtV5H27dutV8/PHH5qqrrjI1atSwn6eeesq89NJL9m9NT9Y+JACqopo1a5pu3brZyNxVVlZmhwsKCiJ+R+OD08vf/va3QHplZWrnBqc5dOiQeffdd6POszqtXyQ7d+605dZq6phsVVlHL+ZZVclaFj2h6qJVnfbhjBkzzNSpU01hYaFtbhysup+HFa1fKp2H8TpG9R0VFaXa/kvUOlbnfdihQwfzwQcf2Nwt93P11Vebyy67zP7dsmXL5O3DuFWn9iE1/VOt9IULF9rmirfddptt+ldUVGSnf/vb33bGjRsX0ky8Ro0azsyZM23zVLWmidQMXvN48cUXnbVr19qa8V42v43n+h0+fNg2sV6xYoVt/vjqq686Xbt2dc466yynpKQk6etXlXU8fvy4bV6sT15enl0f/b158+aY51nd12/MmDHO8uXL7T7UPu/bt6/TpEkTZ8+ePUlfv6qso84xNdl9/vnnQ5oQ6/hMh/OwovVLtfOwsuv3s5/9zFm6dKmzdetWm17XG113FixYkJL7LxHrWN33YbhILdqSsQ8JgE7RL3/5S+crX/mKveCoKeA777wTmHbJJZfYHRvsueeec84++2ybXn2p/PnPfw6ZruZ/999/v9OsWTN7QPXp08fZtGmTkw7rd+zYMefyyy93zjjjDBsYqZm1+ovwIjCo6jrqYqPnhvCP0sU6z+q+foMHD7bBkeanPjw0rL5KvFSZddRxF2kdFbCnw3lY0fql4nlYmfWbMGGC0759eyc7O9tp2LChU1BQYG/AwVJt/8V7Hav7PowlAErGPszQP/HLTwIAAEh91AECAAC+QwAEAAB8hwAIAAD4DgEQAADwHQIgAADgOwRAAADAdwiAAACA7xAAAYirjIwM88ILL6TsVm3durWZNWuW14sBwGMEQAAqZcSIEWbQoEFRp+/atSvkjfHxprdhK8iK9kmnN4IDSJwaCZw3AB9K9Bu3lyxZYk6cOGH/3rFjh+nZs6d59dVXzbnnnht4OWMi6bcT/RsAEo8cIAAJKwL7+OOP7bCCFr3tuU6dOqZz585mxYoVId958803Te/evU3t2rXt26B/+MMfmqNHj0acf6NGjWyQpc8ZZ5xhxzVu3Dgw7vXXX7fBUK1atWxx189//vNyl/fgwYPmu9/9rp1X/fr1zde//nXz/vvvB6ZPnjzZdOnSxTz++OP2LdXZ2dl2vN60ftFFF5kGDRrY37/yyivN1q1bA9+Ldd3feustm2ul6Q0bNjT9+/c3n332WeCt2tOmTbO/q22j7z///POV3CMAIiEAApBwEyZMMGPHjjVr1qwxZ599thk6dKj58ssv7TQFDQMGDDDXX3+9Wbt2rVm8eLENiH7wgx9U+ndWrVplvvnNb5ohQ4aYDz74wAYv999/v1m4cGHU79x4441mz5495i9/+Yv9fteuXU2fPn3MgQMHAmm2bNli/vCHP9hgRusgCtBGjx5t/vnPf5ply5aZzMxMc+2119qgJdZ11zj9Vn5+vg2MtN5XXXWVKS0ttdMV/Dz11FNm/vz5Zt26debuu+82N998s/n73/9e6W0DIExcX60KIO1FenNzMF1W/vjHP4a8Xf7xxx8PTF+3bp0dt2HDBjt8yy23OLfddlvIPN544w0nMzPT+fzzz8tdFnf+//rXv+zwTTfd5PTr1y8kzY9//GMnPz8/MKw3Zz/yyCOB36lfv75TUlIS8p127do5v/rVr+zfeou63ri9Z8+ecpdl7969dlk++OCDmNd96NChzoUXXhhxflqmOnXqOG+//XbIeG0vfQ/AqSEHCEDCderUKfB3Xl6e/V+5LqLiJuXQnH766YGPioGUk7Jt27ZK/c6GDRvMhRdeGDJOw5s3bw7kqgTTbx85csQWYQX/vn43uDirVatWgeI2l+ap3Jy2bdvaojMVt8n27dtjXnc3BygS5TodO3bM9OvXL2TZlCMUvGwAqoZK0AAS7rTTTgv8rXox4hYVKQC5/fbbbb2fcF/5ylcSulz6bQUly5cvP2ma6va46tate9J0FVUpMFqwYIFp3ry5XZ/zzjsvUEE7lnVXvZ7ylk3+/Oc/mxYtWoRMU/0mAKeGAAiAp1TnZv369aZ9+/anPK+vfvWrtlJxMA2r7k1WVlbE3y4qKjI1atQI5ODEYv/+/WbTpk02+FHlbVH9ncpS7pDqD02ZMuWkaaoXpEBHOUqXXHJJpecNoHwEQAAqrbi4OFAZ2KViJLXgqqx7773XfO1rX7OVntUaS7ktCoj+9re/mdmzZ1dqXmPGjDE9evQwU6dONYMHD7YVizWPuXPnRkzft29fU1BQYPs1mjFjhg2UPv30U5vrogrN3bt3j/g9tdbS+j722GM2B0lByrhx4yq97uPHjzcdO3Y0d9xxh/ne975nm9erFZsqZjdp0sRWnlbFZ+UYqcWZtrsCOhW5DR8+vNK/B+D/EAABqDQVGZ1//vkh42655RbbVLwquSBq1aTWUspNUT3qdu3a2QCmspSj89xzz5mJEyfaIEjByU9/+lPbeWMkKpJ65ZVX7G+PHDnS7N271zalv/jii02zZs2i/o5afD377LO22E7FXuecc4559NFHK90JowKupUuXmvvuu8/2Z6QisV69etm6RaJ1UN0jtQb76KOPbLGc1lHpAZyaDNWEPsV5AAAAVCu0AgMAAL5DAAQAAHyHAAgAAPgOARAAAPAdAiAAAOA7BEAAAMB3CIAAAIDvEAABAADfIQACAAC+QwAEAAB8hwAIAAD4DgEQAADwnf8PL2GzhU8M5UMAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(df[\"config/text_det_box_thresh\"], df[\"WER\"])\n", "plt.xlabel(\"Detection Box Threshold\")\n", "plt.ylabel(\"CER\")\n", "plt.title(\"Effect of Detection Threshold on Word Error Rate\")\n", "plt.show()\n", "\n", "plt.scatter(df[\"config/textline_orientation\"], df[\"WER\"])\n", "plt.xlabel(\"Line Tolerance\")\n", "plt.ylabel(\"WER\")\n", "plt.title(\"Effect of Line Tolerance on Word Error Rate\")\n", "plt.show()\n", "\n", "plt.scatter(df[\"config/line_tolerance\"], df[\"WER\"])\n", "plt.xlabel(\"Line Tolerance\")\n", "plt.ylabel(\"WER\")\n", "plt.title(\"Effect of Line Tolerance on Word Error Rate\")\n", "plt.show()\n", "\n", "plt.scatter(df[\"config/text_det_unclip_ratio\"], df[\"WER\"])\n", "plt.xlabel(\"Detection Box Threshold\")\n", "plt.ylabel(\"CER\")\n", "plt.title(\"Effect of Text detection expansion coefficient on Word Error Rate\")\n", "plt.show()\n", "\n", "plt.scatter(df[\"config/text_rec_score_thresh\"], df[\"WER\"])\n", "plt.xlabel(\"Line Tolerance\")\n", "plt.ylabel(\"WER\")\n", "plt.title(\"Effect of Text recognition threshold on Word Error Rate\")\n", "plt.show()\n" ] }, { "attachments": { "image-2.png": { "image/png": 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" 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" } }, "cell_type": "markdown", "id": "7070a6e6", "metadata": {}, "source": [ "# Graph Interpretatation\n", "\n", "Key observations text_det_box_thresh:\n", "Graph 1 (Character Error Rate):\n", "\n", "![image.png](attachment:image.png)\n", "\n", "Clear positive correlation: lower thresholds yield better CER\n", "Optimal zone appears to be around 0.43–0.46, achieving CER values of ~0.06–0.07\n", "Above 0.65, performance degrades significantly (CER > 0.18)\n", "Some variance exists, but the trend is fairly consistent\n", "\n", "Graph 2 (Word Error Rate):\n", "\n", "![image-2.png](attachment:image-2.png)\n", "\n", "Same general trend, but with considerably more variance/scatter\n", "Best WER (~0.15) also achieved at lower thresholds\n", "The spread widens dramatically as threshold increases, suggesting the model becomes unstable at higher values\n", "Note: Your y-axis is still labeled \"CER\" but the title says \"Word Error Rate\" — you'll want to fix that for your thesis\n", "\n", "A lower detection box threshold means PaddleOCR is more permissive about what it considers a valid text region. For your Spanish business documents, being more inclusive captures more text boxes, reducing missed characters/words. However, setting it too low could introduce noise (false positives), which might explain why the absolute minimum threshold isn't always the best.\n", "Recommendation for your thesis: The sweet spot looks like 0.43–0.46 for detection threshold. You might want to narrow your search space around this range and tune other parameters (like unclip_ratio or recognition thresholds) to squeeze out additional gains." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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 }