Unconstrained Handwritten Character Recognition Using Different Classification Strategies

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) are built and used into three different classification strategies: combination of two 26– class classifiers; a 26–metaclass classifier and a 52– class classifier. Experimental results on the NIST SD19 database show that better recognition performance is achieved by the metaclass classifier in which the uppercase and the lowercase representations of the characters are merged into single classes.