Update ocr_engine.py

ocr_engine.py

@@ -5,30 +5,27 @@ import cv2
 
 reader = easyocr.Reader(['en'], gpu=False)
 
-def extract_weight_from_image(pil_image):
+def extract_weight_from_image(pil_img):
     try:
-        image = np.array(pil_image)
+        img = np.array(pil_img)
 
-        # Grayscale conversion
-        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+        # Convert to grayscale
+        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
 
-        # Resize to make digits clearer
-        resized = cv2.resize(gray, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)
+        # Resize to make characters larger
+        resized = cv2.resize(gray, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
 
-        # Bilateral Filter to reduce noise
-        filtered = cv2.bilateralFilter(resized, 11, 17, 17)
+        # Apply blur and threshold to improve text clarity
+        blurred = cv2.GaussianBlur(resized, (5, 5), 0)
+        _, thresh = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
 
-        # Adaptive Thresholding
-        thresh = cv2.adaptiveThreshold(filtered, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
-                                       cv2.THRESH_BINARY, 11, 2)
-
-        # OCR
+        # OCR detection
         result = reader.readtext(thresh, detail=0)
-        text = " ".join(result)
-        print("OCR Output:", text)
+        combined_text = " ".join(result)
+        print("OCR Text:", combined_text)
 
-        # Extract weight like 25.52 or 123
-        match = re.search(r'\d{1,4}(\.\d{1,2})?', text)
+        # Use regex to extract weight like 52.30, 002.50 etc.
+        match = re.search(r"\b\d{1,4}\.?\d{0,2}\b", combined_text)
         if match:
             return match.group(), 95.0
         else: