SHAPE-MATCHING USING LAT AND ITS APPLICATION TO HANDWRITTEN NUMERAL RECOGNITION

This paper describes an iterative technique for gradually deforming a mask binary image with successive local affine transformation (LAT) operations so as to yield the best match to an input binary image as one new and promising approach toward robust handwritten character recognition. The method uses local shapes in the sense that the LAT of each point at one location is optimized using locations of other points by means of least-squares data fitting using Gaussian window functions. It also uses a multiscale refinement technique that employs decreasing the spread of window functions with each iteration. Especially in handwritten character recognition, structural information is indispensable for robust shape matching or discrimination. The method is enhanced to explicitly incorporate structures by weighting the above least-squares criterion with similarity measures of both topological and geometric features of the mask and input images. Moreover, deformation constraints are imposed on each iteration, not only to promote and stabilize matching convergence but also to suppress an excessive matching process. Shape matching experiments have been successfully carried out using skeletons of totally unconstrained handwritten numerals. Furthermore, to evaluate its shape discrimination ability, this method was applied to recognition tests, and a high recognition rate was obtained.