Binary Decision Clustering for Neural Network Based Optical Character Recognition

摘要

A multiple neural network system for handprinted character recognition ispresented. It consists of a set of input networks which discriminate between all two class pairs, 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 forty-five 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 basis function network, and the probabilistic neural network. A simple majority vote rule was also tested in place of the output network.