Update ocr_engine.py

ocr_engine.py

@@ -9,15 +9,23 @@ def extract_weight_from_image(pil_img):
     try:
         img = np.array(pil_img)
 
+        # Step 1: Convert to grayscale
         gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-        resized = cv2.resize(gray, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
-        blurred = cv2.GaussianBlur(resized, (5, 5), 0)
-        _, thresh = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
 
-        result = reader.readtext(thresh, detail=0)
-        text = " ".join(result)
+        # Step 2: Apply adaptive threshold to handle lighting
+        thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                                       cv2.THRESH_BINARY_INV, 11, 2)
+
+        # Step 3: Dilate to make digits thicker
+        kernel = np.ones((2, 2), np.uint8)
+        dilated = cv2.dilate(thresh, kernel, iterations=1)
+
+        # Step 4: OCR on the preprocessed image
+        result = reader.readtext(dilated, detail=0)
+        text = " ".join(result).strip()
         print("OCR Text:", text)
 
+        # Step 5: Match numeric values like 52.30 or 003.25
         match = re.search(r"\b\d{2,4}\.?\d{0,2}\b", text)
         if match:
             return match.group(), 95.0