Update ocr_engine.py

ocr_engine.py

@@ -9,31 +9,34 @@ def extract_weight_from_image(pil_img):
     try:
         img = np.array(pil_img)
 
-        # Resize if image is too large
+        # Resize for consistency
         if img.shape[1] > 1000:
             img = cv2.resize(img, (1000, int(img.shape[0] * 1000 / img.shape[1])))
 
-        # Preprocessing
+        # Preprocessing for 7-segment digital font
         gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-        gray = cv2.resize(gray, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
-        gray = cv2.equalizeHist(gray)
-        blurred = cv2.GaussianBlur(gray, (3, 3), 0)
-        thresh = cv2.adaptiveThreshold(
-            blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2
-        )
+        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)
+
+        # Optional inversion for black background with white digits
+        white_pct = np.mean(thresh > 127)
+        if white_pct < 0.5:
+            thresh = cv2.bitwise_not(thresh)
 
         # OCR
         result = reader.readtext(thresh, detail=0)
-        combined_text = " ".join(result)
+        combined_text = " ".join(result).strip()
         print("OCR Text:", combined_text)
 
-        # Regex to match numbers with optional 'kg'
-        match = re.search(r"(\d{1,4}(?:\.\d{1,2})?)\s*(kg|KG|Kg)?", combined_text)
+        # Match number like 25, 65.2, 18.89 etc.
+        match = re.search(r"(\d{1,4}(?:\.\d{1,2})?)", combined_text)
         if match:
             weight = match.group(1)
-            return f"{weight} kg", 95.0
+            return f"{weight} kg", 100.0
         else:
             return "No weight detected kg", 0.0
 
     except Exception as e:
         return f"Error: {str(e)}", 0.0
+