Create ocr_engine.py

ocr_engine.py

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+import easyocr
+import numpy as np
+import re
+import cv2
+
+reader = easyocr.Reader(['en'], gpu=False)
+
+def extract_weight_from_image(pil_image):
+    try:
+        image = np.array(pil_image)
+
+        # Grayscale conversion
+        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+
+        # Resize to make digits clearer
+        resized = cv2.resize(gray, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)
+
+        # Bilateral Filter to reduce noise
+        filtered = cv2.bilateralFilter(resized, 11, 17, 17)
+
+        # Adaptive Thresholding
+        thresh = cv2.adaptiveThreshold(filtered, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                                       cv2.THRESH_BINARY, 11, 2)
+
+        # OCR
+        result = reader.readtext(thresh, detail=0)
+        text = " ".join(result)
+        print("OCR Output:", text)
+
+        # Extract weight like 25.52 or 123
+        match = re.search(r'\d{1,4}(\.\d{1,2})?', text)
+        if match:
+            return match.group(), 95.0
+        else:
+            return "No weight detected", 0.0
+
+    except Exception as e:
+        return f"Error: {str(e)}", 0.0