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Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers

Alabbas, Maytham; Khudeyer, Raidah; Jaf, Sardar; Dong, Minghui; Tseng, Yuen-Hsien; Lu, Yanfeng; Yu, Liang-Chih; Lee, Lung-Hao; Wu, Chung-Hsien; Li, Haizhou

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Authors

Maytham Alabbas

Raidah Khudeyer

Sardar Jaf

Minghui Dong

Yuen-Hsien Tseng

Yanfeng Lu

Liang-Chih Yu

Lung-Hao Lee

Chung-Hsien Wu

Haizhou Li



Abstract

In this paper, we investigate a range of strategies for combining multiple machine learning techniques for recognizing Arabic characters, where we are faced with imperfect and dimensionally variable input characters. Experimental results show that combined confidence-based backoff strategies can produce more accurate results than each technique produces by itself and even the ones exhibited by the majority voting combination.

Citation

Alabbas, M., Khudeyer, R., Jaf, S., Dong, M., Tseng, Y., Lu, Y., …Li, H. (2017). Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers. In Proceedings of the 2016 International Conference on Asian Language Processing (IALP), 21-23 November 2016, Tainan, Taiwan (262-265). https://doi.org/10.1109/ialp.2016.7875982

Presentation Conference Type Conference Paper (Published)
Conference Name The 20th International Conference on Asian Language Processing.
Start Date Nov 21, 2016
End Date Nov 23, 2016
Acceptance Date Aug 28, 2016
Online Publication Date Mar 13, 2017
Publication Date Mar 13, 2017
Deposit Date Oct 21, 2016
Publicly Available Date Oct 24, 2016
Publisher Institute of Electrical and Electronics Engineers
Pages 262-265
Book Title Proceedings of the 2016 International Conference on Asian Language Processing (IALP), 21-23 November 2016, Tainan, Taiwan.
DOI https://doi.org/10.1109/ialp.2016.7875982
Public URL https://durham-repository.worktribe.com/output/1149517

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