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UWO ORCCA TR-09-01 Summary

Improved Character Recognition through Subclassing and Runoff Elections
Oleg Golubitsky and Stephen M. Watt

Abstract: A succinct representation of handwritten symbol curves can be obtained by computing truncated Legendre-Sobolev expansions of the coordinate functions. With this representation, symbol classes are well linearly separable, which yields fast and robust classification algorithms based on linear support vector machines. However, the presence of different variants of a symbol in the same class reduces linear separability and correct retrieval rates of the linear SVM classifiers. We show experimentally that, by labeling the variants, we obtain nearly 100% linearly separable classes and improve the correct retrieval rates by about 3%. Furthermore, the large gap between the top-1 and top-2 correct retrieval rates can be significantly reduced by replacing the conventional majority voting scheme with a runoff election scheme.

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