ocr_processing.py

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

# Skew Correction
def deskew(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray = cv2.bitwise_not(gray)
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
    coords = np.column_stack(np.where(thresh > 0))
    angle = cv2.minAreaRect(coords)[-1]
    if angle < -45:
        angle = -(90 + angle)
    else:
        angle = -angle
    (h, w) = image.shape[:2]
    center = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D(center, angle, 1.0)
    rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
    return rotated

# Lighting Correction
def correct_lighting(image):
    lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
    l, a, b = cv2.split(lab)
    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
    cl = clahe.apply(l)
    limg = cv2.merge((cl,a,b))
    final = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
    return final

# OCR Extraction using EasyOCR
def extract_text(image, langs=['en']):
    reader = easyocr.Reader(langs, gpu=False)
    results = reader.readtext(image, detail=0, paragraph=True)
    text = "\n".join(results)
    return text

    
def enhance_for_ocr(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Adaptive Threshold
    th = cv2.adaptiveThreshold(
        gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
        cv2.THRESH_BINARY, 15, 10
    )
    return th


# Full pipeline
def process_image(file, langs=['en']):
    img = Image.open(file).convert('RGB')
    img_cv = np.array(img)

    # 1. تصحيح الانحراف
    img_cv = deskew(img_cv)

    # 2. تصحيح الإضاءة
    img_cv = correct_lighting(img_cv)

    # 3. تحسين للنصوص
    img_cv = enhance_for_ocr(img_cv)

    # 4. استخراج النصوص
    text = extract_text(img_cv, langs)
    return text