Representation and recognition of handwritten digits using deformable templates

We investigate the application of deformable templates to recognition of handprinted digits. Two characters are matched by deforming the contour of one to fit the edge strengths of the other, and a dissimilarity measure is derived from the amount of deformation needed, the goodness of fit of the edges, and the interior overlap between the deformed shapes. Classification using the minimum dissimilarity results in recognition rates up to 99.25 percent on a 2,000 character subset of NIST Special Database 1. Additional experiments on an independent test data were done to demonstrate the robustness of this method. Multidimensional scaling is also applied to the 2,000/spl times/2,000 proximity matrix, using the dissimilarity measure as a distance, to embed the patterns as points in low-dimensional spaces. A nearest neighbor classifier is applied to the resulting pattern matrices. The classification accuracies obtained in the derived feature space demonstrate that there does exist a good low-dimensional representation space. Methods to reduce the computational requirements, the primary limiting factor of this method, are discussed.