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J. Eisenstein, S. Ghandeharizadeh, L. Golubchik, C. Shahabi, D. Yan and R. Zimmermann, “Device Independence and Extensibility in Gesture Recognition,” Proceedings of IEEE Virtual Reality, Los Angeles, 22-26 March 2003, pp. 207-214. doi:10.1109/VR.2003.1191141

has been cited by the following article:

• TITLE: Recognition of Arabic Sign Language (ArSL) Using Recurrent Neural Networks

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

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

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.4 No.1, February 28, 2012

  ABSTRACT: 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.