Update ocr_engine.py

ocr_engine.py

@@ -3,37 +3,30 @@ from PIL import Image
 import torch
 import re
 
-# Load TrOCR processor and model once
+# Load TrOCR once
 processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
 model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
 
 def clean_ocr_text(text):
-    # Fix common OCR mistakes
-    text = text.replace(",", ".")               # comma to dot
-    text = re.sub(r"[^\d\.kg]", "", text.lower())  # keep only digits, dot, k, g
+    print("[RAW OCR]", text)
+    text = text.replace(",", ".").replace("s", "5").replace("o", "0").lower()
+    text = re.sub(r"[^\d\.kg]", "", text)  # Keep only digits, dot, k, g
+    print("[CLEANED OCR]", text)
     return text
 
 def extract_weight(image):
     try:
-        # TrOCR inference
         pixel_values = processor(images=image, return_tensors="pt").pixel_values
         generated_ids = model.generate(pixel_values)
         raw_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
-        print("OCR Raw Output:", raw_text)
 
-        # Clean and normalize text
-        cleaned_text = clean_ocr_text(raw_text)
-        print("Cleaned OCR:", cleaned_text)
-
-        # Flexible regex to catch even minor issues (e.g., 52.2g, 98.7kg)
-        pattern = r'(\d{1,5}(?:\.\d{1,3})?)\s*(kg|g)'
-        match = re.search(pattern, cleaned_text)
+        cleaned = clean_ocr_text(raw_text)
 
+        # Regex for weight
+        match = re.search(r'(\d{1,5}(?:\.\d{1,3})?)\s*(kg|g)', cleaned)
         if match:
-            value = match.group(1)
-            unit = match.group(2)
-            return f"{value} {unit}"
+            return f"{match.group(1)} {match.group(2)}"
         else:
-            return "No valid weight found"
+            return f"No valid weight found | OCR: {cleaned}"
     except Exception as e:
         return f"Error: {str(e)}"