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Proceedings Paper

Achieving high recognition reliability using decision trees and AdaBoost
Author(s): Jianying Xiang; Xiao Tu; Yue Lu; Patrick S. P. Wang
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Paper Abstract

Recognition rate is traditionally used as the main criterion for evaluating the performance of a recognition system. High recognition reliability with low misclassification rate is also a must for many applications. To handle the variability of the writing style of different individuals, this paper employs decision trees and WRB AdaBoost to design a classifier with high recognition reliability for recognizing Bangla handwritten numerals. Experiments on the numeral images obtained from real Bangladesh envelopes show that the proposed recognition method is capable of achieving high recognition reliability with acceptable recognition rate.

Paper Details

Date Published: 28 January 2008
PDF: 12 pages
Proc. SPIE 6815, Document Recognition and Retrieval XV, 68150V (28 January 2008); doi: 10.1117/12.766059
Show Author Affiliations
Jianying Xiang, East China Normal Univ. (China)
Xiao Tu, East China Normal Univ. (China)
Yue Lu, East China Normal Univ. (China)
Patrick S. P. Wang, Northeastern Univ. (United States)


Published in SPIE Proceedings Vol. 6815:
Document Recognition and Retrieval XV
Berrin A. Yanikoglu; Kathrin Berkner, Editor(s)

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