This paper describes a system that recognizes hand-printed digits. The system is based on optimal bounded error matching, a technique already in common use in general-purpose 2D and 3D visual object recognition systems in cluttered, noisy scenes. In this paper, we demonstrate that the same techniques achieve high recognition rates (up to 99.2%) on real-world data (the NIST database of hand-printed census forms and the CEDAR database of digits extracted from U.S. mail ZIP codes). As part of the system, we describe a post-processing step for $k$-nearest neighbor classifiers based on decision trees that can be used (in place of the usual heuristic methods) for setting thresholds and improves recognition rates significantly.