University of Bologna

Dipartimento di Elettronica, Informatica e Sistemistica

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Improved Handwritten Character Recognition Using 2nd Order Information from Training Set

Zs. M. Kovacs and R. Ragazzoni and R. Rovatti and R. Guerrieri

Abstract:

In this paper the problem of improving the capability of statistical character classifiers based on finite and sparse training set is addressed. A significant improvement is obtained coupling standard classifiers based on the k-Nearest Neighbors technique with a second higher level classification stage. This method has been applied to three existing classifiers reducing the error rate at zero rejection of about 17%.

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