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: 282 ms (started: 2024-07-29 19:01:29 +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: 610 ms (started: 2024-07-29 19:01:30 +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: 0 ns (started: 2024-07-29 19:01:30 +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": 4,
   "id": "92778211",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xavca\\PycharmProjects\\img2table\\venv\\lib\\site-packages\\torchvision\\models\\_utils.py:135: UserWarning: Using 'weights' as positional parameter(s) is deprecated since 0.13 and may be removed in the future. Please use keyword parameter(s) instead.\n",
      "  warnings.warn(\n",
      "C:\\Users\\xavca\\PycharmProjects\\img2table\\venv\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 14.4 s (started: 2024-07-29 19:01:30 +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\", 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: 610 ms (started: 2024-07-29 19:01:45 +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": 6,
   "id": "148f1e35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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",
      "image/png": "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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: 31 ms (started: 2024-07-29 19:01:45 +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": 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, 672, 1452, 972),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, 672, 1452, 972),shape=(5, 4))]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1 s (started: 2024-07-29 19:01:45 +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",
    "\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=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=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=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: 16 ms (started: 2024-07-29 19:01:46 +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": 9,
   "id": "e869f327",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.09 s (started: 2024-07-29 19:01:46 +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": 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: 31 ms (started: 2024-07-29 19:01:48 +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\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}