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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References
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Tereschenko, V.V.: Development and realization of new principles of handprint documents automatic recognition for computer systems, Moscow (1999) (in Russian)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning. Springer, New York (2001)
Itskovich, L.A., Kolotienko, S.S., Kuznetsov, S.O.: Character recognition using concept lattices: realization and comparison with the other approaches. In: Proceedings of 51th MIPT Scientific Conference, Moscow (2008) (in Russian)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Los Altos (1993)
Nguyen, H.S., Nguyen, S.H.: Fast split selection method and its application in decision tree construction from large databases. International Journal of Hybrid Intelligent Systems 2, 149–160 (2005)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs (2010)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
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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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