Hand gestures enabling deaf people to communication during their daily lives rather than by speaking. A sign language is a language which, instead of using sound, uses visually transmitted gesture signs which simultaneously combine hand shapes, orientation and movement of the hands, arms, lip-patterns, body movements and facial expressions to express the speaker&\#39;s thoughts. Recognizing and documenting Arabic sign language has only been paid attention to recently. There have been few attempts to develop recognition systems to allow deaf people to interact with the rest of society. This paper introduces an automatic Arabic sign language (ArSL) recognition system based on the Hidden Markov Models (HMMs). A large set of samples has been used to recognize 20 isolated words from the Standard Arabic sign language. The proposed system is signer-independent. Experiments are conducted using real ArSL videos taken for deaf people in different clothes and with different skin colors. Our system achieves an overall recognition rate reaching up to 82.22\%.
%0 Journal Article
%1 IJACSA.2011.021108
%A Aliaa A.A Youssif Amal Elsayed Aboutabl, Heba Hamdy Ali
%D 2011
%J International Journal of Advanced Computer Science and Applications(IJACSA)
%K (ArSL); Arabic Contours. Features; Gesture; HMM; Hand Language Sign Tracking;
%N 11
%T Arabic Sign Language (ArSL) Recognition System Using HMM
%U http://ijacsa.thesai.org/
%V 2
%X Hand gestures enabling deaf people to communication during their daily lives rather than by speaking. A sign language is a language which, instead of using sound, uses visually transmitted gesture signs which simultaneously combine hand shapes, orientation and movement of the hands, arms, lip-patterns, body movements and facial expressions to express the speaker&\#39;s thoughts. Recognizing and documenting Arabic sign language has only been paid attention to recently. There have been few attempts to develop recognition systems to allow deaf people to interact with the rest of society. This paper introduces an automatic Arabic sign language (ArSL) recognition system based on the Hidden Markov Models (HMMs). A large set of samples has been used to recognize 20 isolated words from the Standard Arabic sign language. The proposed system is signer-independent. Experiments are conducted using real ArSL videos taken for deaf people in different clothes and with different skin colors. Our system achieves an overall recognition rate reaching up to 82.22\%.
@article{IJACSA.2011.021108,
abstract = {Hand gestures enabling deaf people to communication during their daily lives rather than by speaking. A sign language is a language which, instead of using sound, uses visually transmitted gesture signs which simultaneously combine hand shapes, orientation and movement of the hands, arms, lip-patterns, body movements and facial expressions to express the speaker\&\#39;s thoughts. Recognizing and documenting Arabic sign language has only been paid attention to recently. There have been few attempts to develop recognition systems to allow deaf people to interact with the rest of society. This paper introduces an automatic Arabic sign language (ArSL) recognition system based on the Hidden Markov Models (HMMs). A large set of samples has been used to recognize 20 isolated words from the Standard Arabic sign language. The proposed system is signer-independent. Experiments are conducted using real ArSL videos taken for deaf people in different clothes and with different skin colors. Our system achieves an overall recognition rate reaching up to 82.22\%.},
added-at = {2014-02-21T08:00:08.000+0100},
author = {{Aliaa A.A Youssif Amal Elsayed Aboutabl}, Heba Hamdy Ali},
biburl = {https://www.bibsonomy.org/bibtex/25abd4281308100a5fdfb0f3e2515745a/thesaiorg},
interhash = {353a1bef7436a1756f3505a710be37e8},
intrahash = {5abd4281308100a5fdfb0f3e2515745a},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA)},
keywords = {(ArSL); Arabic Contours. Features; Gesture; HMM; Hand Language Sign Tracking;},
number = 11,
timestamp = {2014-02-21T08:00:08.000+0100},
title = {{Arabic Sign Language (ArSL) Recognition System Using HMM}},
url = {http://ijacsa.thesai.org/},
volume = 2,
year = 2011
}