Binary decision clustering for neural-network-based optical character recognition

Abstract

A multiple neural network system for handprinted character recognition is presented. It consists of a set of input networks which discriminate between all two-class pairs, for example “1” from “7”, and an output network which takes the signals from the input networks and yields a digit recognition decision. For a ten-digit classification problem this requires 45 binary decision machines in the input network. The output stage is typically a single trained network. The neural network paradigms adopted in these input and output networks are the multi-layer perceptron, the radial-bias function network and the probabilistic neural network. A simple majority vote rule was also tested in place of the output network. The various resulting digit classifiers were trained on 7480 isolated images and tested on a disjoint set of size 23140. The Karhunen-Loève transforms of the images of each pair of two classes formed the training set for each BDM. Several different combinations of neural network input and output structures gave similar classification performance. The minimum error rate achieved was 2.5% with no rejection obtained by combining a PNN input array with an RBF output stage. This combined network had an error rate of 0.7% with 10% rejection.