IDEAS home Printed from https://ideas.repec.org/a/igg/jmdem0/v7y2016i2p1-20.html
   My bibliography  Save this article

An Improved Arabic Handwritten Recognition System using Deep Support Vector Machines

Author

Listed:
  • Mohamed Elleuch

    (National School of Computer Science (ENSI), University of Manouba, Manouba, Tunisia)

  • Monji Kherallah

    (Faculty of Sciences, University of Sfax, Sfax, Tunisia)

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.

Suggested Citation

  • Mohamed Elleuch & Monji Kherallah, 2016. "An Improved Arabic Handwritten Recognition System using Deep Support Vector Machines," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 7(2), pages 1-20, April.
  • Handle: RePEc:igg:jmdem0:v:7:y:2016:i:2:p:1-20
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2016040101
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abdallah Ghourabi & Mahmood A. Mahmood & Qusay M. Alzubi, 2020. "A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages," Future Internet, MDPI, vol. 12(9), pages 1-16, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jmdem0:v:7:y:2016:i:2:p:1-20. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.