machine learning - How to separate handwriting from background

Question

I want to separate the handwriting from the background as perfectly as possible in images like the following:

It first looks like one can separate the pixels by color, but plotting the pixels by brightness and relative blue content does not give a clear separation:

Using the above separating lines to keep only the pixels in the upper left area (and set the other pixels to white) we get the following result:

The handwriting is not fully extracted, but the (printed) numbers start to appear. So an improvement of the separating lines does not seem to be possible. Any other possibilities to improve the result?

question from:https://stackoverflow.com/questions/65905461/how-to-separate-handwriting-from-background

Answer 1

This is as far as I could get with simple techniques. I'm using thresholding to get the letters plus bits of numbers and contours to filter out the little number bits. I also end up losing the dots on the i's doing this. If you have control over the handwriting, it'd be a lot easier and cleaner to separate out red ink since the black numbers have some blue in them.

kmeans clustering might get you better results, but I've forgotten how to do that in OpenCV :p

import cv2
import numpy as np

# load image
img = cv2.imread("writing.png");

# convert to hsv
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB);
l, a, b = cv2.split(lab);

# threshold on b channel
done = False;
low = 0;
high = 124; # [0, 124, 8] b-channel
size = 8;
while not done:
    # copy image
    copy = b.copy();

    # threshold
    thresh = cv2.inRange(copy, low, high);

    # contours
    _, contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE);

    # filter contours by size
    big_cntrs = [];
    marked = img.copy();
    for contour in contours:
        area = cv2.contourArea(contour);
        if area > size:
            big_cntrs.append(contour);
    cv2.drawContours(marked, big_cntrs, -1, (0, 255, 0), 3);

    # show
    cv2.imshow("original", img);
    cv2.imshow("marked", marked);
    cv2.imshow("thresh", thresh);
    key = cv2.waitKey(1);

    # check keypress
    done = key == ord('z');
    if key == ord('d'):
        high += 1;
    if key == ord('a'):
        high -= 1;
    if key == ord('w'):
        low += 1;
    if key == ord('s'):
        low -= 1;
    if key == ord('e'):
        size += 1;
    if key == ord('q'):
        size -= 1;
    print([low, high, size]);

# create a mask of the contoured image
mask = np.zeros_like(thresh);
mask = cv2.drawContours(mask, big_cntrs, -1, 255, -1);
cv2.imshow("Mask", mask);
cv2.waitKey(0);
cv2.imwrite("masked.png", mask);