def ImagePrep(img):    
    import cv2
    import numpy as np
    
    
    # Image preprocessing
    
    # Gray Scaling
    def get_grayscale(image):
        return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # Removal of Noise
    def remove_noise(image):
        return cv2.medianBlur(image,5)
     
    #thresholding
    def thresholding(image):
        return cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
    
    #dilation
    def dilate(image):
        kernel = np.ones((5,5),np.uint8)
        return cv2.dilate(image, kernel, iterations = 1)
        
    #erosion
    def erode(image):
        kernel = np.ones((5,5),np.uint8)
        return cv2.erode(image, kernel, iterations = 1)
    
    #opening - erosion followed by dilation
    def opening(image):
        kernel = np.ones((5,5),np.uint8)
        return cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
    
    #canny edge detection
    def canny(image):
        return cv2.Canny(image, 100, 200)
    
    #skew correction
    def deskew(image):
        coords = np.column_stack(np.where(image > 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
        
    gray = get_grayscale(img)
    gray = remove_noise(gray)
    gray = thresholding(gray)
    gray = dilate(gray)
    gray = erode(gray)
    gray = opening(gray)
    gray = canny(gray)
    gray = deskew(gray)
    
    return img