ocr_engine.py

import cv2
import pytesseract
import numpy as np
from PIL import Image

def extract_weight_from_image(pil_img):
    try:
        # Convert to OpenCV image
        img = pil_img.convert("RGB")
        img = np.array(img)
        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

        # Crop or resize if needed (optional based on display layout)

        # Convert to grayscale and enhance contrast
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        _, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV)

        # Optional: Resize for better OCR (helps with small digits)
        resized = cv2.resize(thresh, None, fx=2, fy=2, interpolation=cv2.INTER_LINEAR)

        # Apply OCR with proper config for digits
        custom_config = r'--oem 3 --psm 6 -c tessedit_char_whitelist=0123456789.'
        text = pytesseract.image_to_string(resized, config=custom_config)

        # Filter out non-numeric parts
        weight = ''.join(filter(lambda c: c in '0123456789.', text))
        confidence = 95 if weight else 0
        return weight.strip(), confidence

    except Exception as e:
        print("OCR Exception:", str(e))
        return "", 0