Multiple neural net architectures for character recognition

A multiple neural network system (MNNS) for image-based character recognition is presented. The architecture employs network designs consisting of two levels of fixed feature extraction, followed by a three-layer feedforward perceptron for classification. A multiple network architecture is used to combine network responses. This design minimizes the number of free parameters which must be determined by the training set, leading to rapid training and robust recognition. In comparison to a single network trained with back propagation on zip code digits, the MNNS performs significantly better in terms of error rate and reject rate.<>