Research Article | OPEN ACCESS
Offline Handwritten Arabic Character Recognition Using Features Extracted from Curvelet and Spatial Domains
Mazen Abdullah Bahashwan and Syed Abd Rahman Abu-Bakar
Computer Vision, Video and Image Processing Research Lab (CvviP), Department of Electronics and Computer Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia,
Johor, Malaysia
Research Journal of Applied Sciences, Engineering and Technology 2015 2:158-164
Received: March 19, 2015 | Accepted: March 24, 2015 | Published: September 15, 2015
Abstract
Arabic character recognition is a challenging problem in several artificial intelligence applications, especially when recognizing connected cursive letters. Another dimension of complexity is that Arabic characters may form various shapes depending on their positions in the word. As a result, unconstrained handwritten Arabic character recognition has not been well explored. In this study, we propose an efficient algorithm for Arabic character recognition. The new algorithm combines features extracted from curvelet and spatial domains. The curvelet domain is multiscale and multidirectional. Therefore, curvelet domain is efficient in representing edges and curves. Meanwhile, the spatial domain preserves original aspects of the characters. This feature vector is then trained using the back propagation neural network for the recognition task. The proposed algorithm is evaluated using a database containing 5,600 handwritten characters from 50 different writers. A promising average success rate of 90.3% has been achieved. Therefore, the proposed algorithm is suitable for the unconstrained handwritten Arabic character recognition applications.
Keywords:
Character database, curvelet transform, neural network, optical character recognition,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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