Author(s)

Manar Maraqa, Farid Al-Zboun, Mufleh Dhyabat, Raed Abu Zitar

College of Information Technology, Ajloun National University, Ajloun, Jordan;.
College of Information Technology, AL Al-Bayt University, Mafraq, Jordan;.
Department of Management Information Systems, Al-Isra’ Private University, Amman, Jordan;.
School of Engineering and Technology, New York Institute of Technology, Amman, Jordan..

The objective of this research is to introduce the use of different types of neural networks in human hand gesture recognition for static images as well as for dynamic gestures. This work focuses on the ability of neural networks to assist in Arabic Sign Language (ArSL) hand gesture recognition. We have presented the use of feedforward neural networks and recurrent neural networks along with its different architectures; partially and fully recurrent networks. Then we have tested our proposed system; the results of the experiment have showed that the suggested system with the fully recurrent architecture has had a performance with an accuracy rate 95% for static gesture recognition.

Arabic Sign Language; Feedforward Neural Networks; Recurrent Neural Networks; Gesture Recognition

M. Maraqa, F. Al-Zboun, M. Dhyabat and R. Zitar, "Recognition of Arabic Sign Language (ArSL) Using Recurrent Neural Networks," Journal of Intelligent Learning Systems and Applications, Vol. 4 No. 1, 2012, pp. 41-52. doi: 10.4236/jilsa.2012.41004.