A statistical classifier for hand-written character recognition is presented. After a standard preprocessing phase for image binarization and normalization, a distance transform is applied to the normalized image, converting a black and white (B/W) picture into a gray scale one. The latter is used as feature space for a k-Nearest-Neighbor classifier, based on a dissimilarity measure which generalizes the use of the distance transform itself. The classifier has been implemented on a massively parallel processor, the Connection Machine CM-2. Results obtained from the classification of digits extracted from the U.S. Post Office ZIP code database are provided. The system has an accuracy of 96.73% on the digits when no rejection is allowed and it has an accuracy of 98.96% at 1% error rate.

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