Update app.py

app.py

@@ -1,25 +1,20 @@
-import os
-import pytesseract
-
-# Tesseract path from environment variable
-pytesseract.pytesseract.tesseract_cmd = os.getenv("TESSERACT_PATH", "/usr/bin/tesseract")
-
-
 import gradio as gr
 import torch
 import cv2
-import pytesseract
 import numpy as np
 from PIL import Image
+from paddleocr import PaddleOCR  # Import PaddleOCR
 from ultralytics import YOLO
 
-
 # Load model
 model = YOLO("/home/user/app/best.pt")
 
 # Label map
 label_map = {0: "Analog", 1: "Digital", 2: "Non-LP"}
 
+# Initialize PaddleOCR (for Bangla OCR)
+ocr = PaddleOCR(use_angle_cls=True, lang='bn')  # For Bangla language
+
 def process_frame(frame):
     # Resize to YOLO input shape
     input_img = cv2.resize(frame, (640, 640))
@@ -43,12 +38,13 @@ def process_frame(frame):
         cv2.putText(input_img, f"{label}: {percent}", (x1, y1 - 10),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
 
-        # OCR
+        # OCR using PaddleOCR
         cropped = frame[y1:y2, x1:x2]  # Use original frame for OCR
         if cropped.size > 0:
-            gray = cv2.cvtColor(cropped, cv2.COLOR_BGR2GRAY)
-            text = pytesseract.image_to_string(gray, config="--psm 6 -l ben")
-            extracted_texts.append(text.strip())
+            # Convert to RGB and run OCR
+            result = ocr.ocr(cropped, cls=True)
+            for line in result[0]:
+                extracted_texts.append(line[1])  # Get the detected text
             confidences.append(percent)
 
     # Convert to PIL
@@ -84,9 +80,7 @@ interface = gr.Interface(
         gr.Textbox(label="Confidence (%)")
     ],
     title="YOLOv10n License Plate Detector (Bangla)",
-    description="Upload an image or video. Detects license plates and extracts Bangla text using OCR."
+    description="Upload an image or video. Detects license plates and extracts Bangla text using PaddleOCR."
 )
 
 interface.launch()
-
-