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Proceedings Paper

High-speed coarse classification for large character set using a variable candidate selection method
Author(s): Lei Huang; ChangPing Liu; Tao Gao
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Paper Abstract

This paper describes a high-speed coarse classifier, which makes use of a variable candidate selection method. The classifier is applicable to large character set recognition, such as Chinese, Japanese character. In designing the classifier, three strategies are used: lookup table, dimension reduction, and variable number of candidate selection. The classifier points to two directions: speeding up candidate selection and reduce the candidate set as much as possible. Compared with the fixed number candidate selection method, the third strategy can reduce the average candidate length significantly. In addition, we proposed an adaptively threshold estimating algorithm using distance histogram. The performance of this coarse classifier was test on the 863 Testing System. Experimental results verified its affectivity.

Paper Details

Date Published: 24 September 2001
PDF: 6 pages
Proc. SPIE 4554, Object Detection, Classification, and Tracking Technologies, (24 September 2001); doi: 10.1117/12.441652
Show Author Affiliations
Lei Huang, Institute of Automation (China)
ChangPing Liu, Institute of Automation (China)
Tao Gao, Institute of Automation (China)

Published in SPIE Proceedings Vol. 4554:
Object Detection, Classification, and Tracking Technologies
Jun Shen; Sharatchandra Pankanti; Runsheng Wang, Editor(s)

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