Classification of large set of handwritten characters using modified back propagation model

A novel recognition system has been implemented to solve the difficult problem of handwritten numeral recognition. In this system, the Fourier descriptors are used as dominant features, and a modified backpropagation model is applied to classification. A novel backpropagation learning algorithm has been developed, and its performance has been evaluated. The results show that the learning algorithm is superior to the original backpropagation model. The proposed algorithm was able to solve the nonconvergence problem typically occurring with the standard backpropagation approach. The algorithm has been tested on handwritten numerals collected by the US Post Office