Open Access

5. SVM Model Selection Using PSO for Learning Handwritten Arabic Characters

Mamouni El Mamoun^{1, *}, Zennaki Mahmoud¹, Sadouni Kaddour¹
1 D\u00e9partement Informatique Universit\u00e9 des Sciences et de la Technologie d\u2019Oran Mohamed Boudiaf USTO- MB, BP 1505 El M\u2019naoeur, 31000, Oran, Alg\u00e9rie.

* Corresponding Author: Mamouni El Mamoun. Email: .

Journal of Advanced Optics and Photonics https://doi.org/10.32604/cmc.2019.08081

Abstract

Using Support Vector Machine (SVM) requires the selection of several parameters such as multi-class strategy type (one-against-all or one-against-one), the regularization parameter C, kernel function and their parameters. The choice of these parameters has a great influence on the performance of the final classifier. This paper considers the grid search method and the particle swarm optimization (PSO) technique that have allowed to quickly select and scan a large space of SVM parameters. A comparative study of the SVM models is also presented to examine the convergence speed and the results of each model. SVM is applied to handwritten Arabic characters learning, with a database containing 4840 Arabic characters in their different positions (isolated, beginning, middle and end). Some very promising results have been achieved.

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Keywords

SVM, PSO, handwritten Arabic, grid search, character recognition

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