Unconstrained handwritten character recognition using metaclasses of characters

Abstract

In this paper we tackle the problem of unconstrained handwritten character recognition using different classification strategies. For such an aim, four multilayer perceptron classifiers (MLP) were built and used into three different classification strategies: combination of two 26-class classifiers; 26-metaclass classifier; 52-class classifier. Experimental results on the NIST SD19 database have shown that the recognition rate achieved by the metaclass classifier (87.8%) outperforms the other approaches (82.9% and 86.3%).