I've been studying CV for a few months now but I ran into a problem on my second project, I needed to remove the noise from a sequence of numbers, in order to apply ocr. I managed to clean it up, but the numbers lost some internal pixels.

See the initial and current final image.

CodePudding user response：

I managed to solve it, I didn't use the method you mentioned, but it was a good way, I was apprehensive that it would cause an expansion in the characters and wouldn't be good for OCR reading.

This is my final result:

for mrz in mrz_list:
    try:
        thresh = cv2.threshold(mrz, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

        dist = cv2.distanceTransform(thresh, cv2.DIST_L2, 5)
        dist = cv2.normalize(dist, dist, 0, 1.0, cv2.NORM_MINMAX)
        dist = (dist * 255).astype("uint8")

        thresh = cv2.threshold(dist, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
        opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)

        cnts = cv2.findContours(opening.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        cnts = imutils.grab_contours(cnts)
        chars = []
        for c in cnts:
            (x, y, w, h) = cv2.boundingRect(c)
            if w >= 20 and h >= 25:
                chars.append(c)

        chars = np.vstack([chars[i] for i in range(0, len(chars))])
        hull = cv2.convexHull(chars)

        mask = np.zeros(mrz.shape[:2], dtype="uint8")
        cv2.drawContours(mask, [hull], -1, 255, -1)
        mask = cv2.dilate(mask, None, iterations=2)

        final = cv2.bitwise_and(opening, opening, mask=mask)`

Thanks everyone.