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New optical character recognition method based on Hu invariant moments and weighted voting

Kmieć M.

Journal of Applied Computer Science

2011

Vol. 19, nr 1

33-50

Optical character recognition is an important image processing task. Its aim is to enable computers to recognise graphic characters without human supervision. The process of optical symbol recognition is divided into two stages. First, certain features of the character undergoing recognition are extracted, and second, a match to them is searched for in the library of models. This paper looks at Hu invariant moments, a well established set of image features, and discusses their performance in optical character recognition. One approach to using Hu invariant moments in pattern recognition is using a metric function to find the pattern in the library of models, that is of the same class as the pattern considered. In this paper a new classification method is proposed that performs better than the classic method of metric function.