Continuous Arabic Sign Language Recognition in User Dependent Mode

Arabic Sign Language recognition is an emerging field of research. Previous attempts at automatic visionbased recognition 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 exis...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Assaleh, Khaled (author)
مؤلفون آخرون: Shanableh, Tamer (author), Fanaswala, Mustafa (author), Amin, F. (author), Bajaj, H. (author)
التنسيق: article
منشور في: 2010
الموضوعات:
الوصول للمادة أونلاين:http://hdl.handle.net/11073/8832
الوسوم: إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
_version_ 1864513434082607104
author Assaleh, Khaled
author2 Shanableh, Tamer
Fanaswala, Mustafa
Amin, F.
Bajaj, H.
author2_role author
author
author
author
author_facet Assaleh, Khaled
Shanableh, Tamer
Fanaswala, Mustafa
Amin, F.
Bajaj, H.
author_role author
dc.creator.none.fl_str_mv Assaleh, Khaled
Shanableh, Tamer
Fanaswala, Mustafa
Amin, F.
Bajaj, H.
dc.date.none.fl_str_mv 2010
2017-05-04T10:57:11Z
2017-05-04T10:57:11Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv K. Assaleh, T. Shanableh, M. Fanaswala, F. Amin and H. Bajaj, "Continuous Arabic Sign Language Recognition in User Dependent Mode," Journal of Intelligent Learning Systems and Applications, Vol. 2 No. 1, 2010, pp. 19-27. doi: 10.4236/jilsa.2010.21003.
2150-8410
http://hdl.handle.net/11073/8832
10.4236/jilsa.2010.21003
dc.language.none.fl_str_mv en_US
dc.publisher.none.fl_str_mv Scientific Research
dc.relation.none.fl_str_mv http://dx.doi.org/10.4236/jilsa.2010.21003
dc.subject.none.fl_str_mv Pattern recognition
Motion analysis
Image/video processing
Sign language
dc.title.none.fl_str_mv Continuous Arabic Sign Language Recognition in User Dependent Mode
dc.type.none.fl_str_mv Postprint
Peer-Reviewed
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
description Arabic Sign Language recognition is an emerging field of research. Previous attempts at automatic visionbased recognition 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 perplexity 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 proposed work outperforms existing solutions in terms of recognition accuracy.
format article
id aus_304bf731ae5d4d4f61186a092eac523c
identifier_str_mv K. Assaleh, T. Shanableh, M. Fanaswala, F. Amin and H. Bajaj, "Continuous Arabic Sign Language Recognition in User Dependent Mode," Journal of Intelligent Learning Systems and Applications, Vol. 2 No. 1, 2010, pp. 19-27. doi: 10.4236/jilsa.2010.21003.
2150-8410
10.4236/jilsa.2010.21003
language_invalid_str_mv en_US
network_acronym_str aus
network_name_str aus
oai_identifier_str oai:repository.aus.edu:11073/8832
publishDate 2010
publisher.none.fl_str_mv Scientific Research
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
spelling Continuous Arabic Sign Language Recognition in User Dependent ModeAssaleh, KhaledShanableh, TamerFanaswala, MustafaAmin, F.Bajaj, H.Pattern recognitionMotion analysisImage/video processingSign languageArabic Sign Language recognition is an emerging field of research. Previous attempts at automatic visionbased recognition 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 perplexity 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 proposed work outperforms existing solutions in terms of recognition accuracy.Scientific Research2017-05-04T10:57:11Z2017-05-04T10:57:11Z2010PostprintPeer-Reviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfK. Assaleh, T. Shanableh, M. Fanaswala, F. Amin and H. Bajaj, "Continuous Arabic Sign Language Recognition in User Dependent Mode," Journal of Intelligent Learning Systems and Applications, Vol. 2 No. 1, 2010, pp. 19-27. doi: 10.4236/jilsa.2010.21003.2150-8410http://hdl.handle.net/11073/883210.4236/jilsa.2010.21003en_UShttp://dx.doi.org/10.4236/jilsa.2010.21003oai:repository.aus.edu:11073/88322024-08-22T12:08:33Z
spellingShingle Continuous Arabic Sign Language Recognition in User Dependent Mode
Assaleh, Khaled
Pattern recognition
Motion analysis
Image/video processing
Sign language
status_str publishedVersion
title Continuous Arabic Sign Language Recognition in User Dependent Mode
title_full Continuous Arabic Sign Language Recognition in User Dependent Mode
title_fullStr Continuous Arabic Sign Language Recognition in User Dependent Mode
title_full_unstemmed Continuous Arabic Sign Language Recognition in User Dependent Mode
title_short Continuous Arabic Sign Language Recognition in User Dependent Mode
title_sort Continuous Arabic Sign Language Recognition in User Dependent Mode
topic Pattern recognition
Motion analysis
Image/video processing
Sign language
url http://hdl.handle.net/11073/8832