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Complementary features combined in an HMM-based system to recognize handwritten digits

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1 Author(s)
Britto, A.S., Jr. ; Pontificia Univ. Catolica do Parana, Curitiba, Brazil

We combine complementary features based on foreground and background information in an HMM-based classifier to recognize handwritten digits. A zoning scheme based on column and row models provides a way of dividing the digit into zones without making the features size variant. This strategy allows us to avoid the digit normalization, while it provides a way of having information from specific zones of the digit. Recognition rates around 98% have been achieved using 60,000 digit samples of the NIST SD19 database.

Published in:

Image Analysis and Processing, 2003.Proceedings. 12th International Conference on

Date of Conference:

17-19 Sept. 2003