Question

I’d like to separate an image of text into it’s component characters, also as images. For example, using the sample below I’d end up with 14 images.

I’m only going to be using text on a single line, so the y-height is unimportant – what I need to find is the beginning and end of each letter and crop to those coordinates. That way I would also avoid problems with ‘i’,’j’, etc.

I’m new to image processing, and I’m not sure how to go about it. Some form of edge detection? Is there a way to determine contiguous regions of solid colour? Any help is great.

Trying to improve my Python skills and familiarity with some of the many libraries available, so I’m using the Python Imaging Library (PIL), but I’ve also had a look at OpenCV.

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Sample image:

Answer 1

This is not an easy task especially if the background is not uniform. If what you have is an already binary image like the example, it is slightly simpler.

You can start applying a threshold algorithm if your image is not binary (Otsu adaptative threshold works well)

After you can use a labelling algorithm in order to identify each ‘island’of pixels which forms your shapes (each character in this case).

The problem arises when you have noise. Shapes that were labelled but aren’t of your interest. In this case you can use some heuristic to determine when a shape is a character or not (you can use normalized area, position of the object if your text is in a well define place etc). If this is not enough, you will need to deal with more complex staff like shape feature extraction algorithms and some sort of pattern recognition algorithm, like multilayer perceptrons.

To finish, this seems to be an easy task, but depending the quality of your image, it could get harder. The algorithms cited here can easily be found on the internet or also implemented in some libraries like OpenCv.

Any more help, just ask, if I can help of course 😉