Comparison of Expectation Maximization and Naive Bayes Algorithms in Character Recognition

Abstract

Statistical character recognition methods are used very common nowadays in the character recognition. A certain number of features are extracted from characters recognized for the recognition of the character. The classification is performed with the recognition of these features. Extracted features can be considered as input signal of a prediction system. Thus proposed methods for estimation can be used for the recognition. In this study, digit characters are classified with expectation maximization and Naive Bayes methods over Gaussian mixture models. These two methods are compared with each other.