Home           Contact us           FAQs           
     Journal Home     |     Aim & Scope    |    Author(s) Information      |     Editorial Board     |     MSP Download Statistics
2018 (Vol. 15, Issue: 2)
Research Article

Arabic Sign Language Recognition Using Kinect Sensor

1Abdel-Gawad Abdel-Rabouh Abdel-Samie, 1F.A. Elmisery, 1Ayman M.Brisha and 2Ahmed H. Khalil
1Department of Electronics Technology, Faculty of Industrial Education, Beni-Suef University, Beni-Suef
2Department of Electronics and Communications, Faculty of Engineering, Cairo University, Giza, Egypt

DOI: 10.19026/rjaset.15.5292
Submitted Accepted Published
August 8, 2017 September 14, 2017 February 15, 2018

  How to Cite this Article:

1Abdel-Gawad Abdel-Rabouh Abdel-Samie, 1F.A. Elmisery, 1Ayman M.Brisha and 2Ahmed H. Khalil, 2018. Arabic Sign Language Recognition Using Kinect Sensor.  Research Journal of Applied Sciences, Engineering and Technology, 15(2): 57-67.

DOI: 10.19026/rjaset.15.5292

URL: http://www.maxwellsci.com/jp/mspabstract.php?jid=RJASET&doi=rjaset.15.5292


This study introduces a Real Time System for automatic Arabic sign language recognition system based on Dynamic time warping matching algorithm. The communication between human and machines or between people could done using gestures called sign language. The aim of the sign language recognition is to give an exact and convenient mechanism to transcribe sign gestures into meaningful text or speech so that communication between deaf and hearing society can easily be made. In this study we introduce a translator based on Dynamic Time Warping, where each signed word is coordinating and matching among database, then display the text and the corresponding pronunciation of the income sign. We using the Microsoft’s Kinect sensor to catch the sign. We have built our data using a large set of samples for a dictionary of 30 isolated words homemade signs from the Standard Arabic sign language. The system operates in different modes including online, signer-dependent and signer-independent modes. The presented system allows the signer to do signs freely and naturally. Experimental results using real Arabic sign language data collected show that the presented system has higher recognition rate compared with others for all modes. For signer-dependent online case, the system achieves recognition rate of 97.58%. On the other hand, for signer-independent online case, the system achieves a recognition rate of 95.25%.

Abstract PDF HTML

    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.


© The Author(s) 2018

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Current Information
   Sales & Services
Home  |  Contact us  |  About us  |  Privacy Policy
Copyright © 2015. MAXWELL Scientific Publication Corp., All rights reserved