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Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers

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.

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Paper Details

Authors:
Maytham Alabbas, Raidah S. Khudeyer
Submitted On:
25 November 2016 - 3:49am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Sardar Jaf
Paper Code:
82
Document Year:
2016
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Document Files

Poster.pdf

(515 downloads)

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[1] Maytham Alabbas, Raidah S. Khudeyer, "Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1307. Accessed: Aug. 20, 2017.
@article{1307-16,
url = {http://sigport.org/1307},
author = {Maytham Alabbas; Raidah S. Khudeyer },
publisher = {IEEE SigPort},
title = {Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers},
year = {2016} }
TY - EJOUR
T1 - Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers
AU - Maytham Alabbas; Raidah S. Khudeyer
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1307
ER -
Maytham Alabbas, Raidah S. Khudeyer. (2016). Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers. IEEE SigPort. http://sigport.org/1307
Maytham Alabbas, Raidah S. Khudeyer, 2016. Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers. Available at: http://sigport.org/1307.
Maytham Alabbas, Raidah S. Khudeyer. (2016). "Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers." Web.
1. Maytham Alabbas, Raidah S. Khudeyer. Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1307