Spatial Topology Distance Measurement and Shape Rules for Handwritten Character Recognition

This work shows how to set shape rules and convert them into logical rules to skip incorrect templates and reduce the number of candidate templates in the spatial topology distortion method [1, 2, 3, 4, 5]. The recognition rate is also improved by including shape constraints in the self-organizing process. This will drastically reduce the number of computations with improved recognition. We study a self-organization matching approach to accomplish the recognition of hand-printed characters drawn with thick strokes. This approach is used to flex the unknown hand-printed character toward matching its object characters gradually. The extracted character features used in the self-organization matching are center loci, orientation, and major axes of ellipses which fit the inked area of the patterns. Simulations provide encouraging results using the proposed method. In the spatial topology distortion method [1], named STD, the distortion between a candidate template and an unknown pattern can be computed by using the self-organizing algorithm [7]. This distortion is used to rank its candidate. It is cost to obtain such fine distortions for all templates. It is expected that certain incorrect templates can be skipped by imposing the rules among template features. The STD will be operated only for those templates that meet the rules for fine discrimination. We briefly review the hand-printed character recognition techniques for thick strokes and discuss their difficulties. The difficulties are mainly arisen from the various flexible distortions produced during handwriting. Robust techniques on the thinning method, correlation matching, elastic matching, and distance measurement are the main focuses for solving such difficulties.