Update ocr_engine.py

ocr_engine.py

@@ -3,60 +3,56 @@ import numpy as np
 import cv2
 import re
 
-# Load OCR engine
 reader = easyocr.Reader(['en'], gpu=False)
 
+def enhance_image(img):
+    # Resize large image down to avoid OCR failure
+    max_dim = 1000
+    height, width = img.shape[:2]
+    if max(height, width) > max_dim:
+        scale = max_dim / max(height, width)
+        img = cv2.resize(img, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA)
+
+    # Convert to gray
+    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+    # Denoise
+    gray = cv2.fastNlMeansDenoising(gray, h=15)
+
+    # Sharpen
+    kernel = np.array([[0, -1, 0], [-1, 5,-1], [0, -1, 0]])
+    sharp = cv2.filter2D(gray, -1, kernel)
+
+    # Contrast
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+    contrast = clahe.apply(sharp)
+
+    return contrast
+
 def extract_weight_from_image(pil_img):
     try:
         img = np.array(pil_img)
+        enhanced = enhance_image(img)
 
-        # Resize large image if needed
-        max_dim = 1000
-        height, width = img.shape[:2]
-        if max(height, width) > max_dim:
-            scale = max_dim / max(height, width)
-            img = cv2.resize(img, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA)
-
-        # OCR without heavy preprocessing
-        results = reader.readtext(img)
+        results = reader.readtext(enhanced)
         print("DEBUG OCR RESULTS:", results)
 
-        raw_texts = []
+        ocr_texts = [text for _, text, _ in results]
         weight_candidates = []
 
         for _, text, conf in results:
-            original = text
-            cleaned = text.lower().strip()
-
-            # Fix common OCR mistakes
-            cleaned = cleaned.replace(",", ".")
-            cleaned = cleaned.replace("o", "0").replace("O", "0")
-            cleaned = cleaned.replace("s", "5").replace("S", "5")
-            cleaned = cleaned.replace("g", "9").replace("G", "6")
-            cleaned = cleaned.replace("kg", "").replace("kgs", "")
-            cleaned = re.sub(r"[^0-9\.]", "", cleaned)
-
-            raw_texts.append(f"{original} → {cleaned} (conf: {round(conf, 2)})")
+            cleaned = text.lower().replace("kg", "").replace("kgs", "")
+            cleaned = cleaned.replace("o", "0").replace("s", "5").replace("g", "9")
+            cleaned = re.sub(r"[^\d\.]", "", cleaned)
 
-            # Match flexible weight formats like 75.02, 97.2, 102.34
-            if cleaned.count(".") <= 1 and re.match(r"^\d{2,4}(\.\d{1,3})?$", cleaned):
+            if re.fullmatch(r"\d{2,4}(\.\d{1,2})?", cleaned):
                 weight_candidates.append((cleaned, conf))
 
         if not weight_candidates:
-            return "Not detected", 0.0, "\n".join(raw_texts)
+            return "Not detected", 0.0, "\n".join(ocr_texts)
 
-        # Get best weight
         best_weight, best_conf = sorted(weight_candidates, key=lambda x: -x[1])[0]
-
-        # Strip unnecessary leading zeros
-        if "." in best_weight:
-            int_part, dec_part = best_weight.split(".")
-            int_part = int_part.lstrip("0") or "0"
-            best_weight = f"{int_part}.{dec_part}"
-        else:
-            best_weight = best_weight.lstrip("0") or "0"
-
-        return best_weight, round(best_conf * 100, 2), "\n".join(raw_texts)
+        return best_weight, round(best_conf * 100, 2), "\n".join(ocr_texts)
 
     except Exception as e:
         return f"Error: {str(e)}", 0.0, "OCR failed"