Update ocr_engine.py

ocr_engine.py

@@ -3,33 +3,35 @@ import numpy as np
 import cv2
 import re
 
+# Initialize OCR reader once
 reader = easyocr.Reader(['en'], gpu=False)
 
 def extract_weight_from_image(pil_img):
     try:
         img = np.array(pil_img)
 
-        # Convert to grayscale and resize for better clarity
+        # Convert to grayscale
         gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-        gray = cv2.resize(gray, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
 
-        # Histogram equalization
-        gray = cv2.equalizeHist(gray)
+        # Resize for better accuracy
+        gray = cv2.resize(gray, None, fx=2, fy=2, interpolation=cv2.INTER_LINEAR)
 
-        # Adaptive threshold to enhance text
-        thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
-                                       cv2.THRESH_BINARY, 11, 2)
+        # Gaussian blur to reduce noise
+        blurred = cv2.GaussianBlur(gray, (3, 3), 0)
 
-        # Invert for LCD-like contrast
-        thresh = cv2.bitwise_not(thresh)
+        # Invert colors: useful for LCD display images
+        inverted = cv2.bitwise_not(blurred)
 
-        # OCR read
-        result = reader.readtext(thresh, detail=0)
+        # Normalize brightness
+        norm_img = cv2.normalize(inverted, None, 0, 255, cv2.NORM_MINMAX)
+
+        # Perform OCR
+        result = reader.readtext(norm_img, detail=0)
         combined_text = " ".join(result)
         print("OCR Text:", combined_text)
 
-        # Regex to match weight like 25, 46.5, 75.45 etc.
-        match = re.search(r"\b\d{2,4}\.?\d{0,2}\b", combined_text)
+        # Regex to detect numbers (e.g. 25, 75.45, 100.00)
+        match = re.search(r"\b\d{1,4}(\.\d{1,2})?\b", combined_text)
         if match:
             return match.group(), 95.0
         else: