Recognition of Handwritten Digits Using RBF Neural Network

Abstract

Pattern recognition is required in many fields for different purposes. Methods based on Radial basis function (RBF) neural networks are found to be very successful in pattern classification problems. Training neural network is in general a challenging nonlinear optimization problem. Several algorithms have been proposed for choosing the RBF neural network prototypes and training the network. In this paper RBF neural network using decoupling Kalman filter method is proposed for handwritten digit recognition applications. The efficacy of the proposed method is tested on the handwritten digits of different fonts and found that it is successful in recognizing the digits.