Handwritten digit recognition using combination of neural network classifiers

Abstract

A new classification scheme for handwritten digit recognition is proposed. The method is based on combining the decisions of two multilayer perceptron (MLP) artificial neural network classifiers operating on two different feature types. The first feature set is defined on the pseudo Zernike moments of the image whereas the second feature type is derived from the shadow code of the image using a newly defined projection mask. A MLP network is employed to perform the combination task. The performance is tested on a data base of 15000 samples and the advantage of the combination approach is demonstrated.