{
 "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: 422 ms (started: 2026-05-10 20:18:51 +02:00)\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: 188 ms (started: 2026-05-10 20:18:57 +02:00)\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: 2026-05-10 20:19:00 +02:00)\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",
    "  * Surya OCR (<code>python >= 3.10</code>)\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": 7,
   "id": "92778211",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 10.9 s (started: 2026-05-10 20:21:59 +02:00)\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\")\n",
    "\n",
    "# EasyOCR\n",
    "from img2table.ocr import EasyOCR\n",
    "\n",
    "easyocr = EasyOCR(lang=[\"en\"], kw={\"gpu\": False})\n",
    "\n",
    "# RapidOCR\n",
    "from img2table.ocr import RapidOCR\n",
    "\n",
    "paddle_ocr = RapidOCR()\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": 8,
   "id": "bc97c4ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 93 ms (started: 2026-05-10 20:22:15 +02:00)\n"
     ]
    }
   ],
   "source": [
    "img = Image(src=\"data/tables.png\")\n",
    "\n",
    "# Extract tables\n",
    "extracted_tables = img.extract_tables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "148f1e35",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1365x417>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 62 ms (started: 2026-05-10 20:22:17 +02:00)\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": 11,
   "id": "ea97f8d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: [ExtractedTable(title=Example of Data Table 1, bbox=(236, 250, 1443, 544),shape=(5, 4)),\n",
       "  ExtractedTable(title=Example of Data Table 2, bbox=(236, 672, 1452, 972),shape=(5, 4))],\n",
       " 1: [ExtractedTable(title=Example of Data Table 3, bbox=(236, 250, 1443, 544),shape=(5, 4)),\n",
       "  ExtractedTable(title=Example of Data Table 4, bbox=(236, 672, 1452, 972),shape=(5, 4))]}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 828 ms (started: 2026-05-10 20:22:32 +02:00)\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",
    "                                      max_workers=4)\n",
    "\n",
    "extracted_tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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=236, y1=250, x2=1443, y2=544\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=672, x2=1452, y2=972\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=236, y1=250, x2=1443, y2=544\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=672, x2=1452, y2=972\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: 78 ms (started: 2026-05-10 20:22:42 +02:00)\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": 13,
   "id": "e869f327",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.03 s (started: 2026-05-10 20:22:49 +02:00)\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": 14,
   "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: 2026-05-10 20:22:52 +02:00)\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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18c00e7d-797d-48f4-8e3b-2375954d5fff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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