Update ocr_engine.py

ocr_engine.py

@@ -9,27 +9,29 @@ def extract_weight_from_image(pil_img):
     try:
         img = np.array(pil_img)
 
-        # Resize for consistency
+        # Resize if image is large
         if img.shape[1] > 1000:
             img = cv2.resize(img, (1000, int(img.shape[0] * 1000 / img.shape[1])))
 
-        # Preprocessing for 7-segment digital font
+        # Convert to grayscale and upscale
         gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-        gray = cv2.resize(gray, None, fx=3, fy=3, interpolation=cv2.INTER_LINEAR)
-        blur = cv2.GaussianBlur(gray, (3, 3), 0)
-        _, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+        gray = cv2.resize(gray, None, fx=4, fy=4, interpolation=cv2.INTER_CUBIC)
+        gray = cv2.GaussianBlur(gray, (5, 5), 0)
+        gray = cv2.equalizeHist(gray)
 
-        # Optional inversion for black background with white digits
-        white_pct = np.mean(thresh > 127)
-        if white_pct < 0.5:
+        # Thresholding
+        _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+
+        # Invert if needed
+        if np.mean(thresh > 127) < 0.5:
             thresh = cv2.bitwise_not(thresh)
 
         # OCR
         result = reader.readtext(thresh, detail=0)
-        combined_text = " ".join(result).strip()
-        print("OCR Text:", combined_text)
+        print("🔍 OCR Text:", result)
+        combined_text = " ".join(result)
 
-        # Match number like 25, 65.2, 18.89 etc.
+        # Extract weight
         match = re.search(r"(\d{1,4}(?:\.\d{1,2})?)", combined_text)
         if match:
             weight = match.group(1)
@@ -38,5 +40,5 @@ def extract_weight_from_image(pil_img):
             return "No weight detected kg", 0.0
 
     except Exception as e:
+        print("❌ OCR Error:", e)
         return f"Error: {str(e)}", 0.0
-