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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


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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