Recognition of handprinted digits using optimal bounded error matching

A system for recognizing handprinted digits using optimal bounded error matching is described. Bounded error matching is already in common use in general-purpose 2D and 3D visual object recognition and can cope with clutter, occlusions, and noise, important issues also in OCR. The results presented demonstrate that the same techniques achieve high recognition rates (up to 99.2%) on a real-world handprinted digit recognition task (the NIST database of hand-printed census forms and the CEDAR database of digits extracted from US mail ZIP codes). As part of the system, a post-processing step for k-nearest neighbor classifiers based on decision trees is described that can be used (in place of the usual heuristic methods) for setting thresholds and that improves recognition rates significantly