Massively-parallel handwritten character recognition based on the distance transform

Abstract

A new statistical classifier for handwritten 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) into a gray scale picture. The latter is used as feature space for a $\text{k}$-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, Connection Machine CM-2. Classification results of digits extracted from the U.S. Post Office ZIP code database and the upper-case letters of the NIST Test Data 1 are provided. The system has an accuracy of 96.73% on the digits and 94.51% on the upper-case letters when no rejection is allowed and an accuracy of 98.96%, on the digits and 98.72% on the upper-case letters at 1% error rate.