Open Access

Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

EURASIP Journal on Advances in Signal Processing20052005:507614

Received: 29 December 2003

Published: 15 August 2005


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.

Keywords and phrases

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

Authors’ Affiliations

Electrical Engineering Department, American University of Sharjah
Computer Engineering Department, Jordan University of Science and Technology


© Assaleh and Al-Rousan 2005