Extract letters from image

Question

I have a binary image (only 0 and 255 pixels) like the one below.

I want to extract bounding boxes around the letters such as A,B,C and D. The image is large (around 4000x4000) and the letters can be quite small (like B and D above). Moreover, the characters are broken. That is, there are gaps of black pixels within the outline of a character (such as A below).

The image has white noise, which are like streaks of white lines, scattered around the image.

What I have tried -

1. Extracting contours - The issue is that, for broken characters (like "A"), multiple disconnected contours are obtained for a character. I am not able to obtain a contour for the entire character.

2. Dilation to join edges - This solves the disconnected contours (for large characters) to a certain extent. However, with dilation, I lose a lot of information about smaller characters which now appear like blocks of white pixels.

3. I thought of clustering similar pixels but am not able to come up with a well defined solution.

I kindly request for some ideas! Thanks.

Answer 1

How about this procedure?

1. Object detection (e.g. HOG algorithm): Gives you multiple objects
2. Resize obtained objects to equal size (e.g. 28x28 like MNIST dataset)
3. Character classification (e.g. SVM, kNN, deep learning)

The detail is up to you for each process.

+) Search an example of MNIST recognition. The MNIST dataset is a handwritten digit dataset. There are lots of examples about it. (Even for noisy MNIST)