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Author Assaleh, K. ♦ Shanableh, T. ♦ Fanaswala, M. ♦ Amin, F. ♦ Bajaj, H.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Continuous Arabic Sign Language Recognition ♦ User Dependent Mode ♦ Feature Extraction ♦ Collected Database ♦ Previous Attempt ♦ Average Word Recognition Rate ♦ Sign Language Technique ♦ High Perplex-ity Vocabulary ♦ Automatic Vision-based Recog-nition ♦ Accumulated Image Difference ♦ Experimental Result Section ♦ First Continuous Arabic Sign Language ♦ Unrestrictive Grammar ♦ Continuous Arabic Sign Language Database ♦ Hidden Markov Model ♦ Finger Spelling ♦ Sign Language Expert ♦ Isolated Gesture ♦ Research Community ♦ Spatio-temporal Feature Extraction ♦ Pattern Recognition ♦ Presented Work ♦ Recognition Accuracy ♦ Motion Estimation ♦ Arabic Sign Language ♦ Arabic Sign Language Recognition ♦ Pro-posed Work Outperforms
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.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2009-01-01