Performance Improvement Strategies on Minimum Distance Classification for Large-Set Handwritten Character Recognition


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 5, No. 10, pp. 2600-2608, Oct. 1998
10.3745/KIPSTE.1998.5.10.2600,   PDF Download:

Abstract

This paper proposes an algorithm for off-line recognition of handwritten characters, especially effective for large-set characters such as Korean and Chinese characters. The algorithm is based on a minimum distance classification method which is simple and easy to implement but suffers from low recognition performance. Two strategies have been developed to improve its performance; one is multi-stage pre-classification and the other is candidate reordering. Effectiveness of the algorithm has been proven by an experiment with the samples of 574 classes in a handwritten Korean character database named PE92, where 86.0% of recognition accuracy and 15 characters per second of processing speed have been obtained.


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Cite this article
[IEEE Style]
K. S. Hyung, "Performance Improvement Strategies on Minimum Distance Classification for Large-Set Handwritten Character Recognition," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 5, no. 10, pp. 2600-2608, 1998. DOI: 10.3745/KIPSTE.1998.5.10.2600.

[ACM Style]
Kim Soo Hyung. 1998. Performance Improvement Strategies on Minimum Distance Classification for Large-Set Handwritten Character Recognition. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 5, 10, (1998), 2600-2608. DOI: 10.3745/KIPSTE.1998.5.10.2600.