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What is a good thinning algorithm for getting the "skeleton" of characters for OCR?

Hi guys I have a few thousand training examples for my neural network that looks like:

image description

The thickness does vary in my training set. The accuracy of the neural network on the test set isnt bad, as its around 97% but I have problems when the characters are super small, with a high thickness. I want to normalize the characters to have a standard thickness if possible using a thinning algorithm. I have found many papers that talk about them, but never explain in detail how they work. I was wondering if anyone knew a nice way to do this in OpenCV? I would be very greatful! Thanks.

What is a good thinning algorithm for getting the "skeleton" of characters for OCR?

Hi guys I have a few thousand training examples for my neural network that looks like:

image description

The thickness does vary in my training set. The accuracy of the neural network on the test set isnt bad, as its around 97% but I have problems when the characters are super small, with a high thickness. I want to normalize the characters to have a standard thickness if possible using a thinning algorithm. I have found many papers that talk about them, but never explain in detail how they work. I was wondering if anyone knew a nice way to do this in OpenCV? I would be very greatful! Thanks.

What is a good thinning algorithm for getting the "skeleton" of characters for OCR?

Hi guys I have a few thousand training examples for my neural network that looks like:

image description

The thickness does vary in my training set. The accuracy of the neural network on the test set isnt bad, as its around 97% but I have problems when the characters are super small, with a high thickness. I want to normalize the characters to have a standard thickness if possible using a thinning algorithm. I have found many papers that talk about them, but never explain in detail how they work. I was wondering if anyone knew a nice way to do this in OpenCV? I would be very greatful! Thanks.