Recognition of English and Arabic numerals using a dynamic number of hidden neurons

The paper introduces a method of finding the neighborhood of the optimal number of hidden neurons for an error backpropagation neural network with a single hidden layer. It is based on a study of the curvature of the error function, during the training phase of the network. The method assures convergence and bypasses local minimas. Experimental results show the uniqueness of the method's solution regardless of the initial values of the network's parameters. Two neural networks were built, one for recognizing unconstrained handwritten English numerals and the other for Arabic numerals. Recognition results and comparison with other methods are also presented.