Self-organizing neural network character recognition on a massively parallel computer

Two neural-network-based methods are combined to develop font-independent character recognition on a distributed array processor. Feature localization and noise reduction are achieved using least-squares optimized Gabor filtering. The filtered images are then presented to an ART-1-based learning algorithm which produces self-organizing sets of neural network weights used for character recognition. Implementation of these algorithms on a highly parallel computer with 1024 processors allows high-speed character recognition to be achieved in 8 ms/image with greater than 99% accuracy on machine print and 80% accuracy on unconstrained hand-printed characters