Combining Machine Learning Classifiers for the Task of Arabic Characters Recognition

Alabbas, Maytham; Jaf, Sardar; Khudeyer, S. Raidah

Abstract

There is a number of machine learning algorithms for recognizing Arabic characters. In this paper, we investigate a range of strategies for multiple machine learning algorithms for the task of Arabic characters recognition, where we are faced with imperfect and dimensionally variable input characters. We show two different strategies to combining multiple machine learning algorithms: manual backoff strategry and ensemble learning strategy. We show the performance of using individual algorithms and combined algorithms on recognizing Arabic characters. Experimental results show that combined confidence-based strategies can produce more accurate results than each algorithm produces by itself and even the ones exhibited by the majority voting combination.

Citation

Alabbas, M., Jaf, S., & Khudeyer, S. R. (2018). Combining Machine Learning Classifiers for the Task of Arabic Characters Recognition. Journal of Chinese Language and Computing, 28(1), 1-12

• Journal Article Type: Article
• Acceptance Date: May 27, 2018
• Online Publication Date: Jun 30, 2018
• Publication Date: Jun 30, 2018
• Deposit Date: Jun 12, 2018
• Journal: JOURNAL OF CHINESE LANGUAGE AND COMPUTING.
• Print ISSN: 0219-5968
• Publisher: Chinese & Oriental Languages Information Processing Society
• Peer Reviewed: Peer Reviewed
• Volume: 28
• Issue: 1
• Pages: 1-12
• Public URL: https://durham-repository.worktribe.com/output/1329168
• Publisher URL: http://www.colips.org/web/index.php/ijalp-journal?id=52