examples/Basic_usage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b94d6307",
   "metadata": {},
   "source": [
    "### Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5086f512",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 297 ms (started: 2023-12-16 17:32:42 +01:00)\n"
     ]
    }
   ],
   "source": [
    "%load_ext autotime\n",
    "\n",
    "from IPython.display import display_html\n",
    "from PIL import Image as PILImage\n",
    "from openpyxl import load_workbook\n",
    "from io import BytesIO\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90eaf8af",
   "metadata": {},
   "source": [
    "### Documents\n",
    "\n",
    "Documents can be either an image or a PDF file. Check README for supported formats."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2594279d",
   "metadata": {},
   "source": [
    "#### Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e7b95fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 500 ms (started: 2023-12-16 17:32:44 +01:00)\n"
     ]
    }
   ],
   "source": [
    "from img2table.document import Image\n",
    "\n",
    "img_path = \"data/tables.png\"\n",
    "\n",
    "# Definition of image from path\n",
    "img_from_path = Image(src=img_path)\n",
    "\n",
    "# Definition of image from bytes\n",
    "with open(img_path, 'rb') as f:\n",
    "    img_bytes = f.read()\n",
    "img_from_bytes = Image(src=img_bytes)\n",
    "\n",
    "# Definition of image from file-like object\n",
    "img_from_file_like = Image(src=BytesIO(img_bytes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "083c21e4",
   "metadata": {},
   "source": [
    "#### PDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46a763f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 16 ms (started: 2023-12-16 17:32:46 +01:00)\n"
     ]
    }
   ],
   "source": [
    "from img2table.document import PDF\n",
    "\n",
    "pdf_path = \"data/tables.pdf\"\n",
    "\n",
    "# Definition of PDF from path\n",
    "# The optional pages argument enables the extraction of table on specific pages of the PDF\n",
    "pdf_from_path = PDF(src=pdf_path, pages=[0, 1])\n",
    "\n",
    "# Definition of PDF from bytes\n",
    "with open(pdf_path, 'rb') as f:\n",
    "    pdf_bytes = f.read()\n",
    "pdf_from_bytes = PDF(src=pdf_bytes)\n",
    "\n",
    "# Definition of PDF from file-like object\n",
    "pdf_from_file_like = PDF(src=BytesIO(pdf_bytes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42f85a7f",
   "metadata": {},
   "source": [
    "### OCR instances\n",
    "\n",
    "<code>img2table</code> provides supports for several OCR tools and services :\n",
    "  * Tesseract\n",
    "  * PaddleOCR\n",
    "  * EasyOCR\n",
    "  * docTR\n",
    "  * Google Vision\n",
    "  * AWS Textract\n",
    "  * Azure Cognitive Services\n",
    "  \n",
    "For all OCRs except Tesseract, a specific installation is needed. Check README for relevant instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92778211",
   "metadata": {},
   "outputs": [
   {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 10.2 s (started: 2023-12-16 17:32:47 +01:00)\n"
     ]
    }
   ],
   "source": [
    "# Tesseract OCR\n",
    "from img2table.ocr import TesseractOCR\n",
    "\n",
    "tesseract_ocr = TesseractOCR(n_threads=1, lang=\"eng\")\n",
    "\n",
    "# PaddleOCR\n",
    "from img2table.ocr import PaddleOCR\n",
    "\n",
    "paddle_ocr = PaddleOCR(lang=\"en\", kw={\"use_dilation\": True})\n",
    "\n",
    "# EasyOCR\n",
    "from img2table.ocr import EasyOCR\n",
    "\n",
    "easyocr = EasyOCR(lang=[\"en\"], kw={\"gpu\": False})\n",
    "\n",
    "# docTR\n",
    "from img2table.ocr import DocTR\n",
    "\n",
    "doctr = DocTR(detect_language=True, kw={\"detect_orientation\": True})\n",
    "\n",
    "# Google Vision OCR\n",
    "from img2table.ocr import VisionOCR\n",
    "\n",
    "vision_ocr = VisionOCR(api_key=\"***\")\n",
    "\n",
    "# AWS Textract OCR\n",
    "from img2table.ocr import TextractOCR\n",
    "\n",
    "textract_ocr = TextractOCR(aws_access_key_id=\"***\",\n",
    "                           aws_secret_access_key=\"***\",\n",
    "                           aws_session_token=\"***\",\n",
    "                           region=\"eu-west-1\")\n",
    "\n",
    "# Azure Cognitive Services OCR\n",
    "from img2table.ocr import AzureOCR\n",
    "\n",
    "azure_ocr = AzureOCR(endpoint=\"abc.azure.com\",\n",
    "                           subscription_key=\"***\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64139e2e",
   "metadata": {},
   "source": [
    "### Table identification\n",
    "\n",
    "For a document object (image or pdf), the <code>extract_tables</code> method can be called to perform table identification.<br>\n",
    "A list/dict of <code>ExtractedTable</code> objects is returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc97c4ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 94 ms (started: 2023-12-16 17:32:58 +01:00)\n"
     ]
    }
   ],
   "source": [
    "img = Image(src=\"data/tables.png\")\n",
    "\n",
    "# Extract tables\n",
    "extracted_tables = img.extract_tables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "148f1e35",
   "metadata": {},
   "outputs": [
    {
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/NU+6P/yRr/8ACU+Hv+g9pf8A4GR/40f8JT4e/wCg9pf/AIGR/wCNZH/Cs/CH/QI/8mZf/i6P+FZ+EP8AoEf+TMv/AMXRev2X3v8AyDlyr+ap90f/AJI1/wDhKfD3/Qe0v/wMj/xo/wCEp8Pf9B7S/wDwMj/xrI/4Vn4Q/wCgR/5My/8AxdH/AArPwh/0CP8AyZl/+LovX7L73/kHLlX81T7o/wDyRr/8JT4e/wCg9pf/AIGR/wCNH/CU+Hv+g9pf/gZH/jWR/wAKz8If9Aj/AMmZf/i6P+FZ+EP+gR/5My//ABdF6/Zfe/8AIOXKv5qn3R/+SNf/AISnw9/0HtL/APAyP/Gj/hKfD3/Qe0v/AMDI/wDGsj/hWfhD/oEf+TMv/wAXR/wrPwh/0CP/ACZl/wDi6L1+y+9/5By5V/NU+6P/AMka/wDwlPh7/oPaX/4GR/40f8JT4e/6D2l/+Bkf+NZH/Cs/CH/QI/8AJmX/AOLo/wCFZ+EP+gR/5My//F0Xr9l97/yDlyr+ap90f/kjX/4Snw9/0HtL/wDAyP8Axo/4Snw9/wBB7S//AAMj/wAayP8AhWfhD/oEf+TMv/xdH/Cs/CH/AECP/JmX/wCLovX7L73/AJBy5V/NU+6P/wAka/8AwlPh7/oPaX/4GR/40f8ACU+Hv+g9pf8A4GR/41kf8Kz8If8AQI/8mZf/AIuj/hWfhD/oEf8AkzL/APF0Xr9l97/yDlyr+ap90f8A5I1/+Ep8Pf8AQe0v/wADI/8AGj/hKfD3/Qe0v/wMj/xrzbUvB+g2/wAVdI0WKx26fcWjSSw+c53MBLzu3ZH3V6HtXZf8Kz8If9Aj/wAmZf8A4uojUrSvZLTTqdFfCZbQUHKc/eXMtI7Xa7+Rr/8ACU+Hv+g9pf8A4GR/40f8JT4e/wCg9pf/AIGR/wCNZH/Cs/CH/QI/8mZf/i6P+FZ+EP8AoEf+TMv/AMXV3r9l97/yOflyr+ap90f/AJI1/wDhKfD3/Qe0v/wMj/xo/wCEp8Pf9B7S/wDwMj/xrI/4Vn4Q/wCgR/5My/8AxdH/AArPwh/0CP8AyZl/+LovX7L73/kHLlX81T7o/wDyRr/8JT4e/wCg9pf/AIGR/wCNH/CU+Hv+g9pf/gZH/jWR/wAKz8If9Aj/AMmZf/i6P+FZ+EP+gR/5My//ABdF6/Zfe/8AIOXKv5qn3R/+SNf/AISnw9/0HtL/APAyP/Gj/hKfD3/Qe0v/AMDI/wDGsj/hWfhD/oEf+TMv/wAXR/wrPwh/0CP/ACZl/wDi6L1+y+9/5By5V/NU+6P/AMka/wDwlPh7/oPaX/4GR/40f8JT4e/6D2l/+Bkf+NZH/Cs/CH/QI/8AJmX/AOLo/wCFZ+EP+gR/5My//F0Xr9l97/yDlyr+ap90f/kjX/4Snw9/0HtL/wDAyP8Axo/4Snw9/wBB7S//AAMj/wAayP8AhWfhD/oEf+TMv/xdH/Cs/CH/AECP/JmX/wCLovX7L73/AJBy5V/NU+6P/wAka/8AwlPh7/oPaX/4GR/40f8ACU+Hv+g9pf8A4GR/41kf8Kz8If8AQI/8mZf/AIuj/hWfhD/oEf8AkzL/APF0Xr9l97/yDlyr+ap90f8A5I1/+Ep8Pf8AQe0v/wADI/8AGj/hKfD3/Qe0v/wMj/xrI/4Vn4Q/6BH/AJMy/wDxdH/Cs/CH/QI/8mZf/i6L1+y+9/5By5V/NU+6P/yRr/8ACU+Hv+g9pf8A4GR/40f8JT4e/wCg9pf/AIGR/wCNZH/Cs/CH/QI/8mZf/i6P+FZ+EP8AoEf+TMv/AMXRev2X3v8AyDlyr+ap90f/AJI1/wDhKfD3/Qe0v/wMj/xo/wCEp8Pf9B7S/wDwMj/xrI/4Vn4Q/wCgR/5My/8AxdH/AArPwh/0CP8AyZl/+LovX7L73/kHLlX81T7o/wDyRr/8JT4e/wCg9pf/AIGR/wCNH/CU+Hv+g9pf/gZH/jWR/wAKz8If9Aj/AMmZf/i6P+FZ+EP+gR/5My//ABdF6/Zfe/8AIOXKv5qn3R/+SNf/AISnw9/0HtL/APAyP/Gj/hKfD3/Qe0v/AMDI/wDGsj/hWfhD/oEf+TMv/wAXR/wrPwh/0CP/ACZl/wDi6L1+y+9/5By5V/NU+6P/AMka/wDwlPh7/oPaX/4GR/40f8JT4e/6D2l/+Bkf+NZH/Cs/CH/QI/8AJmX/AOLrjfiX4P0Hw/4ct7vS7H7PO92sbN5zvlSjnGGYjqBUVKlaEXJpaep0YXCZbiq0aMJzvLTVRt+Z6T/wlPh7/oPaX/4GR/40f8JT4e/6D2l/+Bkf+NZH/Cs/CH/QI/8AJmX/AOLo/wCFZ+EP+gR/5My//F1d6/Zfe/8AI5+XKv5qn3R/+SNf/hKfD3/Qe0v/AMDI/wDGj/hKfD3/AEHtL/8AAyP/ABrI/wCFZ+EP+gR/5My//F0f8Kz8If8AQI/8mZf/AIui9fsvvf8AkHLlX81T7o//ACRr/wDCU+Hv+g9pf/gZH/jR/wAJT4e/6D2l/wDgZH/jWR/wrPwh/wBAj/yZl/8Ai6P+FZ+EP+gR/wCTMv8A8XRev2X3v/IOXKv5qn3R/wDkjX/4Snw9/wBB7S//AAMj/wAaP+Ep8Pf9B7S//AyP/Gsj/hWfhD/oEf8AkzL/APF0f8Kz8If9Aj/yZl/+LovX7L73/kHLlX81T7o//JGv/wAJT4e/6D2l/wDgZH/jR/wlPh7/AKD2l/8AgZH/AI1kf8Kz8If9Aj/yZl/+Lo/4Vn4Q/wCgR/5My/8AxdF6/Zfe/wDIOXKv5qn3R/8AkjX/AOEp8Pf9B7S//AyP/Gj/AISnw9/0HtL/APAyP/Gsj/hWfhD/AKBH/kzL/wDF0f8ACs/CH/QI/wDJmX/4ui9fsvvf+QcuVfzVPuj/APJGv/wlPh7/AKD2l/8AgZH/AI0f8JT4e/6D2l/+Bkf+NZH/AArPwh/0CP8AyZl/+Lo/4Vn4Q/6BH/kzL/8AF0Xr9l97/wAg5cq/mqfdH/5I1/8AhKfD3/Qe0v8A8DI/8aP+Ep8Pf9B7S/8AwMj/AMayP+FZ+EP+gR/5My//ABdH/Cs/CH/QI/8AJmX/AOLovX7L73/kHLlX81T7o/8AyRr/APCU+Hv+g9pf/gZH/jR/wlPh7/oPaX/4GR/41kf8Kz8If9Aj/wAmZf8A4uj/AIVn4Q/6BH/kzL/8XRev2X3v/IOXKv5qn3R/+SNf/hKfD3/Qe0v/AMDI/wDGj/hKfD3/AEHtL/8AAyP/ABrI/wCFZ+EP+gR/5My//F0f8Kz8If8AQI/8mZf/AIui9fsvvf8AkHLlX81T7o//ACRr/wDCU+Hv+g9pf/gZH/jR/wAJT4e/6D2l/wDgZH/jWR/wrPwh/wBAj/yZl/8Ai6P+FZ+EP+gR/wCTMv8A8XRev2X3v/IOXKv5qn3R/wDkjX/4Snw9/wBB7S//AAMj/wAaP+Ep8Pf9B7S//AyP/Gsj/hWfhD/oEf8AkzL/APF0f8Kz8If9Aj/yZl/+LovX7L73/kHLlX81T7o//JGv/wAJT4e/6D2l/wDgZH/jR/wlPh7/AKD2l/8AgZH/AI1kf8Kz8If9Aj/yZl/+Lo/4Vn4Q/wCgR/5My/8AxdF6/Zfe/wDIOXKv5qn3R/8AkjX/AOEp8Pf9B7S//AyP/Gj/AISnw9/0HtL/APAyP/Gsj/hWfhD/AKBH/kzL/wDF0f8ACs/CH/QI/wDJmX/4ui9fsvvf+QcuVfzVPuj/APJGv/wlPh7/AKD2l/8AgZH/AI0f8JT4e/6D2l/+Bkf+NZH/AArPwh/0CP8AyZl/+Lo/4Vn4Q/6BH/kzL/8AF0Xr9l97/wAg5cq/mqfdH/5I1/8AhKfD3/Qe0v8A8DI/8aP+Ep8Pf9B7S/8AwMj/AMayP+FZ+EP+gR/5My//ABdH/Cs/CH/QI/8AJmX/AOLovX7L73/kHLlX81T7o/8AyRr/APCU+Hv+g9pf/gZH/jR/wlPh7/oPaX/4GR/41kf8Kz8If9Aj/wAmZf8A4uj/AIVn4Q/6BH/kzL/8XRev2X3v/IOXKv5qn3R/+SMb4byxz+KfGk0MiyRSXoZHQ5VgXmIII6ivRq82+GVrDZeI/GFpbpsggu1jjXJO1VeUAZPPQV6TSwv8JX8/zY88t9ely7Wj/wCkRCiiiug8kKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPMfBWq6dpni/xn9vv7W08y/wDk8+ZY92JJc4yecZH513H/AAlPh7/oPaX/AOBkf+NeeeF/DOj+I/F/jD+1bP7R5F+fL/eOm3dJLn7pGegrrP8AhWfhD/oEf+TMv/xdcVB1eT3Ura9+59Lmiy94l+2lPmtG9lG3wru0a/8AwlPh7/oPaX/4GR/40f8ACU+Hv+g9pf8A4GR/41kf8Kz8If8AQI/8mZf/AIuj/hWfhD/oEf8AkzL/APF1tev2X3v/ACPP5cq/mqfdH/5I1/8AhKfD3/Qe0v8A8DI/8aP+Ep8Pf9B7S/8AwMj/AMayP+FZ+EP+gR/5My//ABdH/Cs/CH/QI/8AJmX/AOLovX7L73/kHLlX81T7o/8AyRr/APCU+Hv+g9pf/gZH/jR/wlPh7/oPaX/4GR/41kf8Kz8If9Aj/wAmZf8A4uj/AIVn4Q/6BH/kzL/8XRev2X3v/IOXKv5qn3R/+SNf/hKfD3/Qe0v/AMDI/wDGj/hKfD3/AEHtL/8AAyP/ABrI/wCFZ+EP+gR/5My//F0f8Kz8If8AQI/8mZf/AIui9fsvvf8AkHLlX81T7o//ACRr/wDCU+Hv+g9pf/gZH/jR/wAJT4e/6D2l/wDgZH/jWR/wrPwh/wBAj/yZl/8Ai6P+FZ+EP+gR/wCTMv8A8XRev2X3v/IOXKv5qn3R/wDkjm/AutaVZ+KfGE1zqdnBFcXoaF5Z1VZBvlOVJPI5HT1Fd3/wlPh7/oPaX/4GR/415t4N8H6DqviPxTaXtj5sFjd+Xbr5zrsXfIMZDAnhR1z0rsv+FZ+EP+gR/wCTMv8A8XWNB1vZ+6l179z0c1jl31p+0lO9o7KNvhVuvY1/+Ep8Pf8AQe0v/wADI/8AGj/hKfD3/Qe0v/wMj/xrI/4Vn4Q/6BH/AJMy/wDxdH/Cs/CH/QI/8mZf/i62vX7L73/kedy5V/NU+6P/AMka/wDwlPh7/oPaX/4GR/40f8JT4e/6D2l/+Bkf+NZH/Cs/CH/QI/8AJmX/AOLo/wCFZ+EP+gR/5My//F0Xr9l97/yDlyr+ap90f/kjX/4Snw9/0HtL/wDAyP8Axo/4Snw9/wBB7S//AAMj/wAayP8AhWfhD/oEf+TMv/xdH/Cs/CH/AECP/JmX/wCLovX7L73/AJBy5V/NU+6P/wAka/8AwlPh7/oPaX/4GR/40f8ACU+Hv+g9pf8A4GR/41kf8Kz8If8AQI/8mZf/AIuj/hWfhD/oEf8AkzL/APF0Xr9l97/yDlyr+ap90f8A5I1/+Ep8Pf8AQe0v/wADI/8AGj/hKfD3/Qe0v/wMj/xrI/4Vn4Q/6BH/AJMy/wDxdH/Cs/CH/QI/8mZf/i6L1+y+9/5By5V/NU+6P/yRjfDeWOfxT40mhkWSKS9DI6HKsC8xBBHUV6NXm3wytYbLxH4wtLdNkEF2sca5J2qrygDJ56CvSaWF/hK/n+bHnlvr0uXa0f8A0iIUUUV0HkhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeeav/yXHQP+vBv/AEGevQ6881f/AJLjoH/Xg3/oM9dVpGvf2rrWuad9m8r+y50h8zfu83dGr5xgYxux36VhR3n6/oj1cz+DD/8AXtf+lSNmiqS3N8dae1bT8WAgDre+cvzSZIMezqMDB3dOcVdrc8oKKKKACiuc8XeLoPCunyTCyuL+7WCSdbaAY+RBlnZzwij1PPOACeK2dNvP7Q0u0vfL8v7RCkuzOdu5QcZ79aHoC1LVFFRzTRW0Ek80ixxRqXd2OAqgZJNAbElFcVa+M9c1W3XUdH8H3F1pL/NFPLexwzTJ/eSJh0PbcykiuzjbfGrlWXcAdrdR7GgB1FY3i7V59A8I6tq1qkbz2dq80ayglSQMjIBBx+NadpK09nBMwAaSNWIHTJGaAJqK5L/hOoZvGNjoNrp1xLBcvPEb9/kjEkSksqAjL4IwSMAHucGuto62DpcKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPPNX/wCS46B/14N/6DPXodeeav8A8lx0D/rwb/0Geuq0jXv7V1rXNO+zeV/Zc6Q+Zv3ebujV84wMY3Y79Kwo7z9f0R6uZ/Bh/wDr2v8A0qRs0VSW5vjrT2rafiwEAdb3zl+aTJBj2dRgYO7pzirtbnlBRRRQAUVzni7xdB4V0+SYWVxf3awSTrbQDHyIMs7OeEUep55wATxWzpt5/aGl2l75fl/aIUl2Zzt3KDjPfrQ9AWpaooqOaaK2gknmkWOKNS7uxwFUDJJoDYkorirXxnrmq266jo/g+4utJf5op5b2OGaZP7yRMOh7bmUkV2cbb41cqy7gDtbqPY0AOorG8XavPoHhHVtWtUjeeztXmjWUEqSBkZAIOPxrTtJWns4JmADSRqxA6ZIzQBNRXEav471PQ4m1K/8ACl1DoMcoSW8a5TzkUttEhhGTtyf72cdq7ZWDqGU5BGQaHo7AtVcWiiigAooooAKKKKACiiigAooooAK88+Mn/IoWn/X+n/ouSvQ688+Mn/IoWn/X+n/ouSsMV/Bkerkf/Ixo+p6HRWNrmvf2Le6Nb/ZvO/tK9Fpu37fLyjNu6HP3cY469auarc31pYNNp2n/AG+5DKBB5yxZBYBjubjgZPvjFb9bf1/Wh5RdooooAKKKgvb2306xnvLuTy7eBC8j4JwB7Dk/hQBPRXNeF/Fp8S3+r2x0u5sP7PkjUC5IEkgdN4JT+DgjgknnnB4rpaACiiuY1fxZPb622h6JpEmranHEJp185YYrdD93fIQcE9lAJxzQB09FZeiahqN/byHVNHk0y4jfb5ZnWZHGM7lZeo+oB9q1KACisDw/rlzqus+IrOeOJY9NvVt4SgILKYkfLZJycsemKl1zVtWsZYrfSNAk1OZ1LszXCwQoB2LkE7j6BT74oA2qKwvCviVPE+mzzmzlsrq1uHtbq1lIYxSp1GRww5GCK3aACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPPPh5/yN/jf/AK/x/wCjJq9Drzz4ef8AI3+N/wDr/H/oyavQ6ww38NfP82ernX++y9If+kRCiiitzygooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA88+Hn/I3+N/+v8f+jJq9Dry/wlcahba/46l0uwjvroagu2CS48lSPMmz8+1sYHtWlp/jTxZqOr6jpsXg6zE2nyRpcE6zwN6hhj9zzwaww38JP1/Nnq51rjZLyh/6RE76iiitzygorMl1y1i8S2+gskv2qe1e6VgBsCIyqQTnOcsO1aEsnlQvJtd9iltqDLHHYDuaOtgvpcfRXFnxvqdjqWnR614Xn07T9RuFtre5N2krpI2dokjX7mcdmbHeu0oDzCiiigAooooA88+Hn/I3+N/+v8f+jJq9Dry/wlcahba/46l0uwjvroagu2CS48lSPMmz8+1sYHtWlp/jTxZqOr6jpsXg6zE2nyRpcE6zwN6hhj9zzwaww38JP1/Nnq51rjZLyh/6RE76iiitzygorMl1y1i8S2+gskv2qe1e6VgBsCIyqQTnOcsO1adABRXJal45jsvG2neHILBrlbiUQ3F2Jdq28hRnVMYO5iqkkZGAR611tD0dg6XCiiigAooooA88+Hn/ACN/jf8A6/x/6Mmr0OvPPh5/yN/jf/r/AB/6Mmr0OsMN/DXz/Nnq51/vsvSH/pEQooorc8oKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDzzV/8AkuOgf9eDf+gz1o+Ef+Rz8b/9f8H/AKTpWdq//JcdA/68G/8AQZ63NQ+H3hrU9UudSubO4+13JDTPDf3EQcgBQSqOB0AHSsKP2/X9D1cz+HD/APXtf+lMoqT/AMLjvhnj/hH4v/R715/aeHLGH4Gw+KA1y2t2lobi2vGuH3QFX4VBnAXjGMc5Oa9jttB020v1vobci5W0WyEjSOx8lSSFOSc8k89feoh4X0YeGT4cFn/xKTEYTb+Y/wBwnJG7O79a6b3ld7afgkjyX8Nlv/wW/wBTifEYv9f8e22jyadY6jZR6St2tnfXjwRSOzlWchY33lQAMEcbs966PwTo2qaJBqNvfraw2r3Aks7S3unuFtkKjKBnVTjcCQMYGa1NW8NaRriQLf2Yka3/ANTKkjRyx+u2RCGGe+DzU+laNp+iWzQadbCFHbe53FmkbAG5mYkscAcknpUdvl+VitNf663M7x3/AMk/8R/9g24/9FtV3w3/AMivpP8A15w/+gCoPEHhPRvFCRpq9vNMkasoVLqWIENjIIRhu6d81JoXhrS/DVvJBpUM0UUhBKyXMs3QYGN7Ngewpy1tbpf8bf5CXX5fr/mM1CXxKmoqum2Wky2OF3SXN3JHID3wqxMPpz+VVfH9rc3vw+1+3swzXEljKEVerfKcgfUZFdHRTi7STFJXi0ZHhe/s9Q8KaXeWTobV7WMoVPCgKAR+GMfhXKXWnWXi74kanpeuZutPsLG3ltLFpCIpDIW3SlQfmIICjOcfjWxN8N/CNxdy3D6NGDM2+WJJZEikPq0SsEP4ir+seENB1427ahp6u9suyGSKR4XjX+6GQgge2cVMVyx5V/WxUneXMec3imx8H/EvRbWaSXSdPjK2e+Qv5JaHc8QJycKccds4r1fTf+QXaf8AXFP/AEEVRj8LaJF4dm0CLTootLmRklgjJXeG+9kg5yfXOfetaONIYkiQYRFCqPQCi2q+f42F/wAD8DjvEn/JSPBP+9e/+ia7OuTvfht4W1HUXv7qyunumdpPMGo3K7Wb720CQBc+gwK6i3gjtbaK3iBEcSBEDMWIAGByeT9TTe4LYkooooAKKKKACiiigAooooAKKKKACiiigAooooA881f/AJLjoH/Xg3/oM9aPhH/kc/G//X/B/wCk6Vnav/yXHQP+vBv/AEGetzUPh94a1PVLnUrmzuPtdyQ0zw39xEHIAUEqjgdAB0rCj9v1/Q9XM/hw/wD17X/pTKKk/wDC474Z4/4R+L/0e9ef2nhyxh+BsPigNctrdpaG4trxrh90BV+FQZwF4xjHOTmvY7bQdNtL9b6G3IuVtFshI0jsfJUkhTknPJPPX3qIeF9GHhk+HBZ/8SkxGE2/mP8AcJyRuzu/Wum95Xe2n4JI8l/DZb/8Fv8AU4nxGL/X/Htto8mnWOo2UekrdrZ3148EUjs5VnIWN95UADBHG7Peuj8E6NqmiQajb362sNq9wJLO0t7p7hbZCoygZ1U43AkDGBmtTVvDWka4kC39mJGt/wDUypI0csfrtkQhhnvg81PpWjafols0GnWwhR23udxZpGwBuZmJLHAHJJ6VHb5flYrTX+utzO8d/wDJP/Ef/YNuP/RbVd8N/wDIr6T/ANecP/oAqDxB4T0bxQkaavbzTJGrKFS6liBDYyCEYbunfNSaF4a0vw1byQaVDNFFIQSslzLN0GBjezYHsKctbW6X/G3+Ql1+X6/5jNQl8SpqKrptlpMtjhd0lzdyRyA98KsTD6c/lVXx/a3N78Ptft7MM1xJYyhFXq3ynIH1GRXR0U4u0kxSV4tGR4Xv7PUPCml3lk6G1e1jKFTwoCgEfhjH4Vyl1p1l4u+JGp6XrmbrT7Cxt5bSxaQiKQyFt0pUH5iCAoznH41sTfDfwjcXctw+jRgzNvliSWRIpD6tErBD+Iq/rHhDQdeNu2oaervbLshkikeF41/uhkIIHtnFTFcseVf1sVJ3lzHnN4psfB/xL0W1mkl0nT4ytnvkL+SWh3PECcnCnHHbOK9X03/kF2n/AFxT/wBBFUY/C2iReHZtAi06KLS5kZJYIyV3hvvZIOcn1zn3rWjjSKJIkGERQqj0Aotqvn+Nhf8AA/A4fxIx8c37eFbAltMgmRtYvF+6NpDC3Q93JA3f3R15OK7kAKAAMAcCuPT4W+EYt3lWF3EGYuVj1O6QZJyTgSY611sEKW1vHBECI41CKCxY4AwOTyfxpvcFtqSUUUUAFFFFABRRRQAUUUUAFFFFABXnnxk/5FC0/wCv9P8A0XJXodeefGT/AJFC0/6/0/8ARclYYr+DI9XI/wDkY0fU0fHP/Ia8Gf8AYaX/ANEyUnxUJHgC7x/z82v/AKUR1va94b0rxLbQ2+rW7zRwSiaLZM8TI+CMhkYHoT3qjB4F8PW+m3OnraTva3LxvKk17PLlkbcuCzkjBGeCM966L6p+Vvxf+Z5S0373/L/I5zU9DsvEHxentNSEstmuhRO9sJWWOU+dIBvAxuAyeDxWJJfXvh3wL40sdKmkjg07VhaWpaVs20MnlbgH5Kgb2wedvXtXqi6TZLrL6uIMX724tml3HmMMWC4zjqSc4zUcOg6XCuoqtnGyalIZbxJMuszFQpyGyMYUDA4ov71/T8Fb8xW0t6/nf8jidL8I6rp+v6VfWGkaPo8Ucp+2SWupyzNdxFSNrK0Sh2zhgxOeK9HrB03wXoGk3cd1aWJEsOfJ82eSVYcjH7tXYhOCR8oHFa95aQ39lNaXAYwzIY3CuyEgjBwykEfUHNSlrqU9jk/Cv/JQPHP/AF82n/pOtdPqb6klkzaVDaTXeRtS6maNMd8sqsf0rB034deGNJ1NdRsrK5ju1cPvbULh9xAwCwaQhuPUGuqp31F0RS0t9TezB1aC0hutx+S0maVMdvmZVOfwrkvCMi23xB8aWNyQt7Ncw3UYbgyQGIKpHqAQRXdVja54U0PxG0L6rYJNND/qpldo5U9g6EMB7Zo6WDrcx/iPfzWvhuKG3vHtFub+2tbm4ifa8MMkgDEH+HI4z2zWNq3h/TPBviLwxP4bhazub3URa3MEcrEXUJRi7OCTuK4DbuvvXYWfhLQLHSrnS4NLg+x3fNxHIDJ5x9XLElj7k1Do3gnw7oF59s07TVjuQuxZZJXlZF/uqXY7R7DAoeqt5f0/67AtH8/6Xp/mZvgz/kaPG3/YVT/0njrd1mPU7xBbaNrFpp92o3SGa1+0HacgELvXHIPJyOKs2WlWWn3V7c2sHlzX0omuG3E73Chc4J44UDjFUdb8JaH4imin1OxEs8SlUnjleKRV/u70IbHtnFC3b9fzF0S9PyMD4ayiCHXNHmQPf2GoMLy8V9wu5XAYydBg4IBXoMYruaoaRoum6DYLZaVZRWlsCW2RjGSepJ6k+55q/SXUphRRRTEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB558PP+Rv8AG/8A1/j/ANGTV6HXnnw8/wCRv8b/APX+P/Rk1eh1hhv4a+f5s9XOv99l6Q/9IiFFFFbnlBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB558PP+Rv8b/8AX+P/AEZNU2jzTW3iz4iT26b54mt3jX+8wtQQPzqH4ef8jf43/wCv8f8Aoyau2tdJsrLUL6+t4NlzfMjXL7id5Vdq8E4GAMcYrHCu1Ner/M9TO1/tkv8ADH/0mJ5Jo3hzXNc8J6frVjp2kprFxGlyuuSatN9o35DHcBD06gx7toHFdJ4w0ORNZn1/VNHj17SY7RQ1uJ9stkU3F5IlbCtkEZIKt8oxmuhHgPwyt41yumKu5/MMKzSCAvnO7yd3l5zznbUup+DdB1i+e7vrJ5ZZABKouJUjlAGAHRWCvxx8wNbLzPLfl/X9fI4u50Hw1r3xQ0S6bSrW5tb/AESS5zNECZSGiCM2eSwU455r0+SSOCFpJHWONFJZmOAoHcnsKyNY8J6Hr32T+0bBZDaZ8hkdojGCMFQUIO0gDjp7VrSwRTwPBLGkkLqUeNxlWUjBBHcULe78ge1l5nm3jGPVtJvtI1zWtSttV0m31GPyrCC3+zlXdtqSZ3P5jLnOPlHevTa5mw+H3hXTL+K9tdIjWaE7od8ryJCfVEZiqf8AAQK6ah6u/wDXUForBRRRQAUUUUAeefDz/kb/ABv/ANf4/wDRk1TaPNNbeLPiJPbpvnia3eNf7zC1BA/Oofh5/wAjf43/AOv8f+jJq7a10mystQvr63g2XN8yNcvuJ3lV2rwTgYAxxiscK7U16v8AM9TO1/tkv8Mf/SYnkmjeHNc1zwnp+tWOnaSmsXEaXK65Jq032jfkMdwEPTqDHu2gcV0njDQ5E1mfX9U0ePXtJjtFDW4n2y2RTcXkiVsK2QRkgq3yjGa6EeA/DK3jXK6Yq7n8wwrNIIC+c7vJ3eXnPOdtS6n4N0HWL57u+snllkAEqi4lSOUAYAdFYK/HHzA1svM8t+X9f18ji7nQfDWvfFDRLptKtbm1v9EkuczRAmUhogjNnksFOOea9C1y/n0rQb2+tLKW8nt4WeK2hQs0jAcKAOevpVXWPCeh699k/tGwWQ2mfIZHaIxgjBUFCDtIA46e1bIAAAHQULe78ge1l5niM2twadceEBJpPiFrxNUe7vZZdJmRriZ4ZN2wEZY5PCjoq+1e2o2+NXww3AHDDBH1FVb3SrLULmyubqDzJbGUzW7biNjlSueDzwxHOetXKVtb/wBbsd9P68gooopiCiiigDzz4ef8jf43/wCv8f8AoyavQ688+Hn/ACN/jf8A6/x/6Mmr0OsMN/DXz/Nnq51/vsvSH/pEQooorc8oKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDzzV/+S46B/wBeDf8AoM9eh15X400641X4saRZWmoS6fPLYfLcxZ3JjzicYIPIBHXvWj/wrzxD/wBD/qn5Sf8Ax2uOnOcZTUY318vI+ixeHw9Slh3VrKD5Fo1J9Za6JnodFeef8K88Q/8AQ/6p+Un/AMdo/wCFeeIf+h/1T8pP/jta+0qfyfiji+pYL/oJX/gM/wDI9Dorzz/hXniH/of9U/KT/wCO0f8ACvPEP/Q/6p+Un/x2j2lT+T8UH1LBf9BK/wDAZ/5HodFeef8ACvPEP/Q/6p+Un/x2j/hXniH/AKH/AFT8pP8A47R7Sp/J+KD6lgv+glf+Az/yPQ6K88/4V54h/wCh/wBU/KT/AOO0f8K88Q/9D/qn5Sf/AB2j2lT+T8UH1LBf9BK/8Bn/AJHodFeef8K88Q/9D/qn5Sf/AB2j/hXniH/of9U/KT/47R7Sp/J+KD6lgv8AoJX/AIDP/I9Dorzz/hXniH/of9U/KT/47R/wrzxD/wBD/qn5Sf8Ax2j2lT+T8UH1LBf9BK/8Bn/keh0V55/wrzxD/wBD/qn5Sf8Ax2j/AIV54h/6H/VPyk/+O0e0qfyfig+pYL/oJX/gM/8AI9Dorzz/AIV54h/6H/VPyk/+O0f8K88Q/wDQ/wCqflJ/8do9pU/k/FB9SwX/AEEr/wABn/keh0V55/wrzxD/AND/AKp+Un/x2j/hXniH/of9U/KT/wCO0e0qfyfig+pYL/oJX/gM/wDI9Dorzz/hXniH/of9U/KT/wCO0f8ACvPEP/Q/6p+Un/x2j2lT+T8UH1LBf9BK/wDAZ/5HodFeef8ACvPEP/Q/6p+Un/x2j/hXniH/AKH/AFT8pP8A47R7Sp/J+KD6lgv+glf+Az/yPQ6K88/4V54h/wCh/wBU/KT/AOO0f8K88Q/9D/qn5Sf/AB2j2lT+T8UH1LBf9BK/8Bn/AJHodFeef8K88Q/9D/qn5Sf/AB2j/hXniH/of9U/KT/47R7Sp/J+KD6lgv8AoJX/AIDP/I9Dorzz/hXniH/of9U/KT/47R/wrzxD/wBD/qn5Sf8Ax2j2lT+T8UH1LBf9BK/8Bn/kGr/8lx0D/rwb/wBBnr0OvEr/AMK6rB8R9M0h/E95LeT2zSJqDBvMiGJPlHz5x8p7j7x/Hqv+FeeIf+h/1T8pP/jtY0qk05Wh17ryPRzDCYWUaHNiErQVvdlqry12/M9Dorzz/hXniH/of9U/KT/47R/wrzxD/wBD/qn5Sf8Ax2tvaVP5PxR531LBf9BK/wDAZ/5HodFeef8ACvPEP/Q/6p+Un/x2j/hXniH/AKH/AFT8pP8A47R7Sp/J+KD6lgv+glf+Az/yPQ6K88/4V54h/wCh/wBU/KT/AOO0f8K88Q/9D/qn5Sf/AB2j2lT+T8UH1LBf9BK/8Bn/AJHodFeef8K88Q/9D/qn5Sf/AB2j/hXniH/of9U/KT/47R7Sp/J+KD6lgv8AoJX/AIDP/I9Dorzz/hXniH/of9U/KT/47R/wrzxD/wBD/qn5Sf8Ax2j2lT+T8UH1LBf9BK/8Bn/keh0V55/wrzxD/wBD/qn5Sf8Ax2j/AIV54h/6H/VPyk/+O0e0qfyfig+pYL/oJX/gM/8AI9Dorzz/AIV54h/6H/VPyk/+O0f8K88Q/wDQ/wCqflJ/8do9pU/k/FB9SwX/AEEr/wABn/keh0V55/wrzxD/AND/AKp+Un/x2j/hXniH/of9U/KT/wCO0e0qfyfig+pYL/oJX/gM/wDI9Dorzz/hXniH/of9U/KT/wCO0f8ACvPEP/Q/6p+Un/x2j2lT+T8UH1LBf9BK/wDAZ/5HodFeef8ACvPEP/Q/6p+Un/x2j/hXniH/AKH/AFT8pP8A47R7Sp/J+KD6lgv+glf+Az/yPQ6K88/4V54h/wCh/wBU/KT/AOO0f8K88Q/9D/qn5Sf/AB2j2lT+T8UH1LBf9BK/8Bn/AJHodFeef8K88Q/9D/q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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1365x417>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 16 ms (started: 2023-12-16 17:32:58 +01:00)\n"
     ]
    }
   ],
   "source": [
    "# Display extracted tables\n",
    "table_img = cv2.imread(\"data/tables.png\")\n",
    "\n",
    "for table in extracted_tables:\n",
    "    for row in table.content.values():\n",
    "        for cell in row:\n",
    "            cv2.rectangle(table_img, (cell.bbox.x1, cell.bbox.y1), (cell.bbox.x2, cell.bbox.y2), (255, 0, 0), 2)\n",
    "            \n",
    "PILImage.fromarray(table_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "229e9a8e",
   "metadata": {},
   "source": [
    "### Table extraction\n",
    "\n",
    "If an ocr instance is provided to the <code>extract_table</code> method, cells content will be infered from the OCR result.<br>\n",
    "A <code>pd.DataFrame</code> representation of the extracted tables can be accessed via the <code>df</code> attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea97f8d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: [ExtractedTable(title=Example of Data Table 1, bbox=(235, 249, 1442, 543),shape=(5, 4)),\n",
       "  ExtractedTable(title=Example of Data Table 2, bbox=(236, 671, 1451, 971),shape=(5, 4))],\n",
       " 1: [ExtractedTable(title=Example of Data Table 3, bbox=(235, 249, 1442, 543),shape=(5, 4)),\n",
       "  ExtractedTable(title=Example of Data Table 4, bbox=(236, 671, 1451, 971),shape=(5, 4))]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 562 ms (started: 2023-12-16 17:33:03 +01:00)\n"
     ]
    }
   ],
   "source": [
    "pdf = PDF(src=\"data/tables.pdf\")\n",
    "\n",
    "# Extract tables\n",
    "extracted_tables = pdf.extract_tables(ocr=tesseract_ocr,\n",
    "                                      implicit_rows=False,\n",
    "                                      borderless_tables=False,\n",
    "                                      min_confidence=50)\n",
    "\n",
    "extracted_tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3851b0ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3 style=\"text-align: center\">Page 1 - Extracted table n°1</h3>\n",
       "                   <p style=\"text-align: center\">\n",
       "                       <b>Title:</b> Example of Data Table 1<br>\n",
       "                       <b>Bounding box:</b> x1=235, y1=249, x2=1442, y2=543\n",
       "                   </p>\n",
       "                   <div align=\"center\"><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sample</td>\n",
       "      <td>blue LED value</td>\n",
       "      <td>green LED value</td>\n",
       "      <td>red LED value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clear water</td>\n",
       "      <td>97</td>\n",
       "      <td>19</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue water</td>\n",
       "      <td>73</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>green water</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tea water</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "                   <hr>\n",
       "                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3 style=\"text-align: center\">Page 1 - Extracted table n°2</h3>\n",
       "                   <p style=\"text-align: center\">\n",
       "                       <b>Title:</b> Example of Data Table 2<br>\n",
       "                       <b>Bounding box:</b> x1=236, y1=671, x2=1451, y2=971\n",
       "                   </p>\n",
       "                   <div align=\"center\"><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sample</td>\n",
       "      <td>blue % transmitted</td>\n",
       "      <td>green % transmitted</td>\n",
       "      <td>red % transmitted</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clear water</td>\n",
       "      <td>97/97 = 100%</td>\n",
       "      <td>19/19 = 100%</td>\n",
       "      <td>79/79 = 100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue water</td>\n",
       "      <td>73/97 = 75%</td>\n",
       "      <td>11/19 = 58%</td>\n",
       "      <td>13/79 = 17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>green water</td>\n",
       "      <td>35/97 = 36%</td>\n",
       "      <td>15/19 = 79%</td>\n",
       "      <td>14/79 = 18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tea water</td>\n",
       "      <td>33/97 = 34%</td>\n",
       "      <td>13/19 = 68%</td>\n",
       "      <td>70/79 = 91%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "                   <hr>\n",
       "                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3 style=\"text-align: center\">Page 2 - Extracted table n°1</h3>\n",
       "                   <p style=\"text-align: center\">\n",
       "                       <b>Title:</b> Example of Data Table 3<br>\n",
       "                       <b>Bounding box:</b> x1=235, y1=249, x2=1442, y2=543\n",
       "                   </p>\n",
       "                   <div align=\"center\"><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sample</td>\n",
       "      <td>blue LED value</td>\n",
       "      <td>green LED value</td>\n",
       "      <td>red LED value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>+ 1 tsp milk</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>+1tsp milk + blue</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>+1tsp milk +green</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>+1tsp milk + tea</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "                   <hr>\n",
       "                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3 style=\"text-align: center\">Page 2 - Extracted table n°2</h3>\n",
       "                   <p style=\"text-align: center\">\n",
       "                       <b>Title:</b> Example of Data Table 4<br>\n",
       "                       <b>Bounding box:</b> x1=236, y1=671, x2=1451, y2=971\n",
       "                   </p>\n",
       "                   <div align=\"center\"><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Water sample</td>\n",
       "      <td>blue % transmitted</td>\n",
       "      <td>green % transmitted</td>\n",
       "      <td>red % transmitted</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>+ 1 tsp milk</td>\n",
       "      <td>13/13 = 100%</td>\n",
       "      <td>14/14 = 100%</td>\n",
       "      <td>12/12 = 100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>+1tsp milk + blue</td>\n",
       "      <td>10/13 = 77%</td>\n",
       "      <td>9/14 = 64%</td>\n",
       "      <td>6/12 = 50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>+1tsp milk +green</td>\n",
       "      <td>9/13 = 69%</td>\n",
       "      <td>13/14 = 93%</td>\n",
       "      <td>7/12 = 58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>+1tsp milk + tea</td>\n",
       "      <td>7/13 = 54%</td>\n",
       "      <td>9/14 = 69%</td>\n",
       "      <td>9/12 = 75%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "                   <hr>\n",
       "                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 31 ms (started: 2023-12-16 17:33:06 +01:00)\n"
     ]
    }
   ],
   "source": [
    "for page, tables in extracted_tables.items():\n",
    "    for idx, table in enumerate(tables):\n",
    "        display_html(table.html_repr(title=f\"Page {page + 1} - Extracted table n°{idx + 1}\"), raw=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e94d997",
   "metadata": {},
   "source": [
    "### Export to xlsx\n",
    "\n",
    "All tables from a document can be exported in xslx format, with one table per worksheet.<br>\n",
    "Arguments are similar to the <code>extract_tables</code> method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e869f327",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 546 ms (started: 2023-12-16 17:33:10 +01:00)\n"
     ]
    }
   ],
   "source": [
    "pdf = PDF(src=\"data/tables.pdf\")\n",
    "\n",
    "# Export to file\n",
    "pdf.to_xlsx('data/tables.xlsx',\n",
    "            ocr=tesseract_ocr,\n",
    "            implicit_rows=False,\n",
    "            borderless_tables=False,\n",
    "            min_confidence=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b177bacc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Worksheet Page 1 - Table 1 : 5 rows, 4 columns\n",
      "Worksheet Page 1 - Table 2 : 5 rows, 4 columns\n",
      "Worksheet Page 2 - Table 1 : 5 rows, 4 columns\n",
      "Worksheet Page 2 - Table 2 : 5 rows, 4 columns\n",
      "time: 16 ms (started: 2023-12-16 17:33:13 +01:00)\n"
     ]
    }
   ],
   "source": [
    "for ws in load_workbook(\"data/tables.xlsx\"):\n",
    "    print(f\"Worksheet {ws.title} : {len(tuple(ws.rows))} rows, {len(tuple(ws.rows)[0])} columns\")"
   ]
  }
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