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UWO ORCCA TR-09-03 Summary

Distance-Based Classification of Handwritten Symbols
Oleg Golubitsky and Stephen M. Watt

Abstract: We study online classification of isolated handwritten mathematical symbols based on the Euclidean, Manhattan, and elastic matching distances, as well as the distance to the convex hull of nearest neighbors. We show experimentally that the distance to the convex hull of nearest neighbors yields the best classification accuracy of about 97.5%. Any of the above distance measures can be used to find the nearest neighbors and prune totally irrelevant classes, but the Manhattan distance is preferable for this because it admits a very efficient implementation. We use the first few Legendre-Sobolev coefficients of the coordinate functions to represent the symbol curves in a finite-dimensional vector space and choose the optimal dimension and number of bits per coefficient by cross-validation. We discuss an implementation of the proposed classification scheme that will allow classification of a sample among hundreds of classes in a setting with strict time and storage limitations.

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