Improving character recognition rate by a multi-net neural classifier

Abstract

A neural classifier for isolated omnifont characters is discussed. A method for characterizing a given training set of characters, based on the definition of some statistical parameters is introduced; on the basis of such characterization an architecture is defined made of a set of neural networks properly connected. Depending on the value of the parameters characterizing the training set, both sizing and training of each network are separately carried out according to a suitable methodology. It is shown that higher recognition rates can be achieved than those obtained by using a single neural network as classifier.<>