{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d7e50af",
   "metadata": {},
   "source": [
    "### Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb649b04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 437 ms (started: 2026-05-10 20:24:22 +02:00)\n"
     ]
    }
   ],
   "source": [
    "%load_ext autotime\n",
    "\n",
    "from IPython.display import display_html\n",
    "from PIL import Image as PILImage\n",
    "\n",
    "from img2table.document import Image\n",
    "from img2table.ocr import TesseractOCR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec5c901",
   "metadata": {},
   "source": [
    "The <code>implicit_rows</code> parameter is used to split existing rows into smaller ones if:\n",
    "1. The row contains multi-line cells\n",
    "2. Vertical separation between elements of the cell is large enough\n",
    "\n",
    "The same principle is applied at the column level using the <code>implicit_columns</code> parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21336bb3",
   "metadata": {},
   "source": [
    "### Image used for the example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "023b4276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=632x344>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 31 ms (started: 2026-05-10 20:24:23 +02:00)\n"
     ]
    }
   ],
   "source": [
    "PILImage.open(\"data/implicit.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b595d562",
   "metadata": {},
   "source": [
    "### Instantiate objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a172a717",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 235 ms (started: 2026-05-10 20:24:23 +02:00)\n"
     ]
    }
   ],
   "source": [
    "# Define OCR instance, requires prior installation of Tesseract-OCR\n",
    "ocr = TesseractOCR()\n",
    "\n",
    "# Define image\n",
    "img = Image(src=\"data/implicit.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb700fe0",
   "metadata": {},
   "source": [
    "### Extract tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "494e62de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.03 s (started: 2026-05-10 20:24:23 +02:00)\n"
     ]
    }
   ],
   "source": [
    "# Extract tables without implicit rows\n",
    "extracted_tables = img.extract_tables(ocr=ocr, implicit_rows=False, implicit_columns=False)\n",
    "table = extracted_tables.pop()\n",
    "\n",
    "# Extract tables with implicit rows\n",
    "extracted_tables_implicit = img.extract_tables(ocr=ocr, implicit_rows=True, implicit_columns=False)\n",
    "table_implicit_rows = extracted_tables_implicit.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0253eb9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3 style=\"text-align: center\">Regular table</h3>\n",
       "                   <p style=\"text-align: center\">\n",
       "                       <b>Title:</b> No title detected<br>\n",
       "                       <b>Bounding box:</b> x1=64, y1=14, x2=563, y2=316\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Col1</td>\n",
       "      <td>Col 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Row 1</td>\n",
       "      <td>Value 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Implicit row 1\\nImplicit row 2\\nImplicit row 3</td>\n",
       "      <td>Implicit value 1\\nImplicit value 2\\nImplicit value 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Row 3</td>\n",
       "      <td>Value 3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "                   <hr>\n",
       "                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3 style=\"text-align: center\">Table with implicit rows</h3>\n",
       "                   <p style=\"text-align: center\">\n",
       "                       <b>Title:</b> No title detected<br>\n",
       "                       <b>Bounding box:</b> x1=64, y1=14, x2=563, y2=316\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Col1</td>\n",
       "      <td>Col 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Row 1</td>\n",
       "      <td>Value 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Implicit row 1</td>\n",
       "      <td>Implicit value 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Implicit row 2</td>\n",
       "      <td>Implicit value 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Implicit row 3</td>\n",
       "      <td>Implicit value 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Row 3</td>\n",
       "      <td>Value 3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "                   <hr>\n",
       "                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 391 ms (started: 2026-05-10 20:24:24 +02:00)\n"
     ]
    }
   ],
   "source": [
    "display_html(table.html_repr(title=\"Regular table\"), raw=True)\n",
    "display_html(table_implicit_rows.html_repr(title=\"Table with implicit rows\"), raw=True)"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}