Achieving high recognition reliability using decision trees and AdaBoost

Jianying Xiang; Xiao Tu; Yue Lu; Patrick S. P. Wang

doi:10.1117/12.766059

28 January 2008 Achieving high recognition reliability using decision trees and AdaBoost

Jianying Xiang, Xiao Tu, Yue Lu, Patrick S. P. Wang

Proceedings Volume 6815, Document Recognition and Retrieval XV; 68150V (2008) https://doi.org/10.1117/12.766059
Event: Electronic Imaging, 2008, San Jose, California, United States

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.

Citation Download Citation

Jianying Xiang, Xiao Tu, Yue Lu, and Patrick S. P. Wang "Achieving high recognition reliability using decision trees and AdaBoost", Proc. SPIE 6815, Document Recognition and Retrieval XV, 68150V (28 January 2008); https://doi.org/10.1117/12.766059

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KEYWORDS

Reliability

Feature extraction

Image processing

Digital filtering

Image filtering

Detection and tracking algorithms

Computing systems

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