Abstract
In this paper we consider applications of well-known numerical classifiers to the problem of character recognition (optical character recognition, OCR). We discuss the requirements which these classifiers should meet to solve this problem. Various modifications of well-known algorithms are proposed. Recognition rates of these classifiers are compared on real character datasets.
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© 2011 Springer-Verlag Berlin Heidelberg
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Itskovich, L., Kuznetsov, S. (2011). Machine Learning Methods in Character Recognition. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2011. Lecture Notes in Computer Science(), vol 6743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21881-1_50
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DOI: https://doi.org/10.1007/978-3-642-21881-1_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21880-4
Online ISBN: 978-3-642-21881-1
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