car-plate-ocr-fa.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import easyocr\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "reader = easyocr.Reader(['fa'])\n",
    "\n",
    "\n",
    "image_path = '7.jpg'\n",
    "image = cv2.imread(image_path)\n",
    "\n",
    "\n",
    "height, width = image.shape[:2]\n",
    "new_width = 2 * width\n",
    "new_height = 2 * height\n",
    "resized_image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_LINEAR)\n",
    "\n",
    "\n",
    "gray_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "\n",
    "alpha = 1.5\n",
    "beta = 30\n",
    "adjusted_image = cv2.convertScaleAbs(gray_image, alpha=alpha, beta=beta)\n",
    "\n",
    "\n",
    "kernel = np.array([[0, -1, 0], [-1, 5,-1], [0, -1, 0]])\n",
    "sharpened_image = cv2.filter2D(adjusted_image, -1, kernel)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "blurred_image = cv2.GaussianBlur(sharpened_image, (5, 5), 0)\n",
    "\n",
    "\n",
    "_, thresholded_image = cv2.threshold(blurred_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
    "\n",
    "\n",
    "cv2.imwrite('processed_image.jpg', thresholded_image)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "results = reader.readtext('processed_image.jpg')\n",
    "\n",
    "\n",
    "confidence_threshold = 0.5\n",
    "\n",
    "for result in results:\n",
    "    detected_text = result[1]\n",
    "    confidence = result[2]\n",
    "    if confidence >= confidence_threshold:\n",
    "        print(f'Detected text: {detected_text}, Confidence: {confidence:.2f}')\n",
    "    else:\n",
    "        print(f\"Low confidence in detected text: {detected_text}, Confidence: {confidence:.2f}\")\n"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}