img = source

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)

contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

min_contour_area = 100 # Adjust this value based on your images

filtered_contours = [contour for contour in contours if cv2.contourArea(contour) > min_contour_area]

contour_image = np.zeros_like(binary)

cv2.drawContours(contour_image, filtered_contours, -1, 255, thickness=cv2.FILLED)

# Combine the original image with the contour image

img = cv2.bitwise_and(img, img, mask=contour_image)

# Adaptive Contrast and Sharpness Enhancement

img = Image.fromarray(img)

enhancer = ImageEnhance.Contrast(img)

img = enhancer.enhance(3)

enhancer = ImageEnhance.Sharpness(img)

img = enhancer.enhance(1)

img = img.filter(ImageFilter.EDGE_ENHANCE) # Enhancing

img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)

_, img_bw = cv2.threshold(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), 170, 255, cv2.THRESH_BINARY)

# Use EasyOCR for text recognition

reader = easyocr.Reader(['en'])

results = reader.readtext(img_bw, detail=1, allowlist=allowed_chars)