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has been cited by the following article:

• TITLE: Continuous Arabic Sign Language Recognition in User Dependent Mode

  AUTHORS: K. Assaleh, T. Shanableh, M. Fanaswala, F. Amin, H. Bajaj

  KEYWORDS: Pattern Recognition, Motion Analysis, Image/ Video Processing and Sign Language

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.2 No.1, March 1, 2010

  ABSTRACT: Arabic Sign Language recognition is an emerging field of research. Previous attempts at automatic vision-based recog-nition of Arabic Sign Language mainly focused on finger spelling and recognizing isolated gestures. In this paper we report the first continuous Arabic Sign Language by building on existing research in feature extraction and pattern recognition. The development of the presented work required collecting a continuous Arabic Sign Language database which we designed and recorded in cooperation with a sign language expert. We intend to make the collected database available for the research community. Our system which we based on spatio-temporal feature extraction and hidden Markov models has resulted in an average word recognition rate of 94%, keeping in the mind the use of a high perplex-ity vocabulary and unrestrictive grammar. We compare our proposed work against existing sign language techniques based on accumulated image difference and motion estimation. The experimental results section shows that the pro-posed work outperforms existing solutions in terms of recognition accuracy.