Maybe someone has ideas how can we fill black pixels to white on the white digits and make that image more adopted to be recognizable
I was trying the Gaussian Blur with Kernel size (1,1), but it doesn't help effectively, sometimes digits on image become merged and this is much worst result
You may use the equivalent to MATLAB imfill, but the result is going to be binary image.
I found a Python implementation for imfill here (it uses Scikit-image).
Here is the code:
import cv2
import numpy as np
from skimage.morphology import reconstruction
def imfill(img):
# https://stackoverflow.com/questions/36294025/python-equivalent-to-matlab-funciton-imfill-for-grayscale
# Use the matlab reference Soille, P., Morphological Image Analysis: Principles and Applications, Springer-Verlag, 1999, pp. 208-209.
# 6.3.7 Fillhole
# The holes of a binary image correspond to the set of its regional minima which
# are not connected to the image border. This definition holds for grey scale
# images. Hence, filling the holes of a grey scale image comes down to remove
# all minima which are not connected to the image border, or, equivalently,
# impose the set of minima which are connected to the image border. The
# marker image 1m used in the morphological reconstruction by erosion is set
# to the maximum image value except along its border where the values of the
# original image are kept:
seed = np.ones_like(img)*255
img[ : ,0] = 0
img[ : ,-1] = 0
img[ 0 ,:] = 0
img[ -1 ,:] = 0
seed[ : ,0] = 0
seed[ : ,-1] = 0
seed[ 0 ,:] = 0
seed[ -1 ,:] = 0
fill_img = reconstruction(seed, img, method='erosion')
return fill_img
img = cv2.imread('5375.jpg', cv2.IMREAD_GRAYSCALE) # Read image as grayscale
img_thresh = cv2.threshold(img, 0, 255, cv2.THRESH_OTSU)[1] # Convert to B/W
fill_img = imfill(img_thresh)
cv2.imshow('img', img)
cv2.imshow('fill_img', fill_img)
cv2.waitKey()
cv2.destroyAllWindows()
Result:
Note:
You may get the same result using cv2.findContours and drawContours, but you should apply findContours on img_thresh.
In case you want closer result to the original image, you may use closing morphological operation, and use 'fill_img' as a mask:
closed_img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, np.ones((35, 35)))
closed_img[fill_img == 0] = 0 # Set to zero where fill_img is zero.
Result:
