Character recognition based on skeleton analysis

Abstract

Character Recognition is a prominent field of research in pattern recognition. Low error rate of methods presented in other papers indicates that the problem of recognizing typewritten fonts is solved, using mainly deep learning methods. However, those algorithms do not work as well for recognizing handwritten characters, since learning discriminative features is much more complex for this problem so it still remains an interesting issue from research point of view. This document presents a proposal to solve handwritten characters recognition problem using k3m skeletonization algorithm. The idea has been designed to work correctly regardless of the width of the characters, their thickness or shape. This is an innovative method not considered in previous papers, which yields results comparable to the best ones achieved so far, what is proven in tests. The method can be also easily extended to signs other than glyphs in Latin alphabet.