Update ocrr_engine.py

ocrr_engine.py

@@ -1,4 +1,4 @@
-import pytesseract
+import easyocr
 import numpy as np
 import cv2
 import re
@@ -9,8 +9,11 @@ import sys
 # Set up logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[logging.StreamHandler(sys.stdout)])
 
+# Initialize EasyOCR reader (once at module level for efficiency)
+reader = easyocr.Reader(['en'], gpu=False)  # GPU=False for CPU-only environments like Hugging Face Spaces
+
 def preprocess_image(img):
-    """Preprocess image for robust OCR, optimized for various weight display formats."""
+    """Preprocess image for robust OCR with EasyOCR, optimized for weight displays."""
     try:
         # Convert PIL to OpenCV format
         img = np.array(img)
@@ -23,61 +26,68 @@ def preprocess_image(img):
         clahe = cv2.createCLAHE(clipLimit=4.0, tileGridSize=(8, 8))
         enhanced = clahe.apply(gray)
         
-        # Apply adaptive thresholding with flexible block size
-        block_size = max(11, min(31, int(img.shape[0] / 20) * 2 + 1))
+        # Apply adaptive thresholding
+        block_size = max(11, min(31, int(img.shape[0] / 15) * 2 + 1))
         thresh = cv2.adaptiveThreshold(
-            enhanced, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, block_size, 3
+            enhanced, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, block_size, 2
         )
         
-        # Sharpen to enhance edges
-        kernel = np.array([[-1, -1, -1],
-                          [-1, 9, -1],
-                          [-1, -1, -1]])
-        sharpened = cv2.filter2D(thresh, -1, kernel)
-        
-        # Denoise for noisy images
-        denoised = cv2.fastNlMeansDenoising(sharpened, h=10)
-        
-        return denoised
+        return thresh  # EasyOCR handles further processing
     except Exception as e:
         logging.error(f"Preprocessing failed: {str(e)}")
-        return gray  # Fallback to grayscale
+        return gray
 
 def extract_weight_from_image(pil_img):
-    """Extract weight and unit from a digital scale image, supporting various formats."""
+    """Extract weight and unit from a digital scale image using EasyOCR."""
     try:
         # Preprocess image
         thresh = preprocess_image(pil_img)
         
-        # Convert to PIL for Tesseract
-        pil_img = Image.fromarray(cv2.cvtColor(thresh, cv2.COLOR_GRAY2RGB))
+        # Convert to numpy array for EasyOCR
+        img_np = np.array(thresh)
         
-        # Try Tesseract with optimized config for numbers and units
-        config = r'--oem 3 --psm 7 -c tessedit_char_whitelist=0123456789.,kgKg'
-        text = pytesseract.image_to_string(pil_img, config=config)
-        logging.info(f"Tesseract raw output: {text}")
+        # Use EasyOCR to detect text
+        results = reader.readtext(img_np, detail=1, paragraph=False)
+        logging.info(f"EasyOCR raw output: {results}")
         
-        # Clean and validate text
+        # Extract weight and unit from detected text
+        text = " ".join([result[1] for result in results])  # Combine all detected text
         text = text.strip().lower()
-        text = re.sub(r'\s+', '', text)  # Remove extra spaces
+        text = re.sub(r'\s+', ' ', text)  # Normalize spaces
         
-        # Extract weight and unit
-        match = re.search(r'(\d*\.?\d+)([kgkg]?)', text)
+        # Extract weight and unit, prioritizing common formats
+        match = re.search(r'(-?\d*\.?\d+)([kgkg]?)', text)
         if match:
             weight_str = match.group(1)
             unit = match.group(2) if match.group(2) else "g"  # Default to grams if no unit
             weight_str = weight_str.replace(',', '.')  # Handle decimal formats (e.g., 68,0)
-            if re.fullmatch(r'^\d*\.?\d+$', weight_str):
+            if re.fullmatch(r'^-?\d*\.?\d+$', weight_str):
                 weight_str = weight_str.lstrip('0') or '0'
-                confidence = 95.0 if len(weight_str.replace('.', '')) >= 3 else 90.0
+                confidence = min([result[2] for result in results if result[1]] or [0.0]) * 100  # Convert EasyOCR confidence (0-1) to percentage
                 try:
                     weight = float(weight_str)
-                    if 0.001 <= weight <= 5000:
+                    if -5000 <= weight <= 5000:
                         logging.info(f"Detected weight: {weight} {unit}, Confidence: {confidence:.2f}%")
                         return weight_str, confidence, unit
                 except ValueError:
                     logging.warning(f"Invalid weight format: {weight_str}")
         
+        # Fallback to detect numbers without units if no match
+        match_no_unit = re.search(r'(-?\d*\.?\d+)', text)
+        if match_no_unit and not match:
+            weight_str = match_no_unit.group(1)
+            weight_str = weight_str.replace(',', '.')
+            if re.fullmatch(r'^-?\d*\.?\d+$', weight_str):
+                weight_str = weight_str.lstrip('0') or '0'
+                confidence = min([result[2] for result in results if result[1]] or [0.0]) * 100
+                try:
+                    weight = float(weight_str)
+                    if -5000 <= weight <= 5000:
+                        logging.info(f"Detected weight (no unit): {weight} g, Confidence: {confidence:.2f}%")
+                        return weight_str, confidence, "g"
+                except ValueError:
+                    logging.warning(f"Invalid weight format: {weight_str}")
+        
         logging.info("No valid weight detected.")
         return "Not detected", 0.0, ""
     except Exception as e: