{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "93c44a25",
   "metadata": {},
   "source": [
    "# Reuse a pipeline and measure a page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e0284c00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-01T06:44:57.295596Z",
     "iopub.status.busy": "2026-11-01T06:44:57.295452Z",
     "iopub.status.idle": "2026-11-01T06:45:37.414957Z",
     "shell.execute_reply": "2026-11-01T06:45:37.414649Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loaded DBNet detector (CPU, threads=8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loaded SVTR recognizer (vocab=185 chars, CPU, threads=8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[occular] LM (pure Python): compact_lm.npz (268 MB), unigrams=2010937, alpha=0.5 beta=1.0 beam=100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seconds for this page: 12.23\n",
      "Recognized lines: 43\n"
     ]
    }
   ],
   "source": [
    "from time import perf_counter\n",
    "from occular import OCRPipeline\n",
    "\n",
    "reader = OCRPipeline()\n",
    "reader.get_text(\"receipt.jpg\")  # warm up the models\n",
    "\n",
    "started = perf_counter()\n",
    "text = reader.get_text(\"receipt.jpg\")\n",
    "seconds = perf_counter() - started\n",
    "print(\"Seconds for this page:\", round(seconds, 2))\n",
    "print(\"Recognized lines:\", len(text.splitlines()))"
   ]
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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