Skip to main content

Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

Abstract

Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL) alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Khaled Assaleh.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Assaleh, K., Al-Rousan, M. Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers. EURASIP J. Adv. Signal Process. 2005, 507614 (2005). https://doi.org/10.1155/ASP.2005.2136

Download citation

  • Received:

  • Revised:

  • Published:

  • DOI: https://doi.org/10.1155/ASP.2005.2136

Keywords and phrases

  • Arabic sign language
  • hand gestures
  • feature extraction
  • adaptive neuro-fuzzy inference systems
  • polynomial classifiers