Handwritten numeral recognition by multilayered neural network with improved learning algorithm

Abstract

Pattern recognition using multilayered feedforward neural networks is described. The backpropagation learning algorithm for multilayered neural networks was investigated. Results showed that there is a state in which neural networks can learn no more patterns, in spite of there being large errors. The learning algorithm was improved to avoid this problem. In order to estimate the multilayered neural network's ability for pattern recognition, an experiment was carried out on handwritten numeral recognition. Three kinds of neural networks were used. One is a basic multilayered neural network in which each hidden unit is connected to all input units. Another has each hidden unit connected to input units in a local area of a character. The last is a neural network into which feature vectors to be extracted from characters are input. Recognition rates achieved are 98.3%, 98.8%, and 99.1%, respectively.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>