Smoothing extremal points in handwritten digits

Question

I am trying to recognize hand written digits. Say that I have the following image:

My target is to smooth the extremal features of the contours, and keep only the shape of the white trace like below:

I first applied cv2.THRESH_BINARY_INV to remove the noise.

Now I tried applying cv2.erode() with np.ones((5,5)) as the kernel, but the resulting figure still had the extremal points.

I think applying cv2.findContours() may help to get the desired shape, but I am going to end up with two contours, one for the inner and another for the outer part. Any ideas will be much appreciated!

Edit: Thanks to @stateMachine, I managed to get a skeleton of the digit. I applied cv2.ximgproc.thinning(), followed by cv2.GaussianBlur() and cv2.MORPH_CLOSE. If the extremal points of this image can be smoothened a bit then it would be perfect. I am still open to any ideas :)

Answer 1

Maybe what you are looking for is the shape's skeleton. The skeleton is part of OpenCV's extended image processing module (pip install opencv-contrib-python). You can compute the skeleton of your image like this:

# Imports:
import cv2

# Image path
path = "D://opencvImages//"
fileName = "OKwfZ.png"

# Reading an image in default mode:
inputImage = cv2.imread(path + fileName)

# To Grayscale:
grayscaleImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)

# Compute the skeleton:
skeleton = cv2.ximgproc.thinning(grayscaleImage, None, 1)

cv2.imshow("Skeleton", skeleton)
cv2.waitKey(0)

This is the result:

The skeleton normalizes the thickness of the image to 1 pixel. If you need a thicker line you can apply some dilations.