Recognition of unconstrained handwritten numerals by a radial basis function neural network classifier

Abstract

Among the neural network models the RBF (Radial Basis Function) network seems to be quite effective for a pattern recognition task such as handwritten numeral recognition since it is extremely flexible to accommodate various and minute variations in data. Recently we obtained a good recognition rate for handwritten numerals by using an RBF network. In this paper we show how to design an RBF network classifier for a given problem in a well defined and easy-to-follow manner. We also report on the experiments to evaluate the performance of the RBF network classifier so designed.