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An Improved Arabic Handwritten Recognition System Using Deep Support Vector Machines

An Improved Arabic Handwritten Recognition System Using Deep Support Vector Machines

Mohamed Elleuch, Monji Kherallah
Copyright: © 2018 |Pages: 23
ISBN13: 9781522552048|ISBN10: 1522552049|EISBN13: 9781522552055
DOI: 10.4018/978-1-5225-5204-8.ch025
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MLA

Elleuch, Mohamed, and Monji Kherallah. "An Improved Arabic Handwritten Recognition System Using Deep Support Vector Machines." Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2018, pp. 656-678. https://doi.org/10.4018/978-1-5225-5204-8.ch025

APA

Elleuch, M. & Kherallah, M. (2018). An Improved Arabic Handwritten Recognition System Using Deep Support Vector Machines. In I. Management Association (Ed.), Computer Vision: Concepts, Methodologies, Tools, and Applications (pp. 656-678). IGI Global. https://doi.org/10.4018/978-1-5225-5204-8.ch025

Chicago

Elleuch, Mohamed, and Monji Kherallah. "An Improved Arabic Handwritten Recognition System Using Deep Support Vector Machines." In Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 656-678. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5204-8.ch025

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Abstract

Deep learning algorithms, as a machine learning algorithms developed in recent years, have been successfully applied in various domains of computer vision, such as face recognition, object detection and image classification. These Deep algorithms aim at extracting a high representation of the data via multi-layers in a deep hierarchical structure. However, to the authors' knowledge, these deep learning approaches have not been extensively studied to recognize Arabic Handwritten Script (AHS). In this paper, they present a deep learning model based on Support Vector Machine (SVM) named Deep SVM. This model has an inherent ability to select data points crucial to classify good generalization capabilities. The deep SVM is constructed by a stack of SVMs allowing to extracting/learning automatically features from the raw images and to perform classification as well. The Multi-class SVM with an RBF kernel, as non-linear discriminative features for classification, was chosen and tested on Handwritten Arabic Characters Database (HACDB). Simulation results show the effectiveness of the proposed model.

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