Handwritten numeral recognition using neural networks: improving the accuracy by discriminative training

Artificial neural networks have been widely used in handwritten numeral recognition (HNR) with some success. This paper presents some new results of HNR using MLP (multilayer perceptron) and RBF (radial basis function) neural networks. By using discriminative training, which aims to minimize the empirical classification error on a training data set, the recognition accuracy is considerably improved. The performance of the RBF net is comparable to that of the MLP in terms of the forced recognition rate, and even better than the MLP in terms of the rejection-error tradeoff. The experiments were implemented on the CENPARMI database, and very high recognition rates have been obtained