Hand Gesture Recognition: Sign to Voice System (S2V)
Hand gesture is one of the typical methods used in sign language for non-verbal communication. It is most commonly used by people who have hearing or speech problems to communicate among themselves or with normal people. Various sign language systems have been developed by manufacturers around the g...
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| Format: | Article |
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World Academy of Science, Engineering and Technology (WASET)
2009
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| Online Access: | http://scholars.utp.edu.my/id/eprint/1731/ |
| _version_ | 1848659162652737536 |
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| author | Foong, Oi Mean |
| author_facet | Foong, Oi Mean |
| author_sort | Foong, Oi Mean |
| building | UTP Institutional Repository |
| collection | Online Access |
| description | Hand gesture is one of the typical methods used in sign language for non-verbal communication. It is most commonly used by people who have hearing or speech problems to communicate among themselves or with normal people. Various sign language systems have been developed by manufacturers around the globe but they are neither flexible nor cost-effective for the end users. This paper presents a system prototype that is able to automatically recognize sign language to help normal people to communicate more effectively with the hearing or speech impaired people. The Sign to Voice system prototype, S2V, was developed using Feed Forward Neural Network for two-sequence signs detection. Different sets of universal hand gestures were captured from video camera and utilized to train the neural network for classification purpose. The experimental results have shown that neural network has achieved satisfactory result for sign-to-voice translation. |
| first_indexed | 2025-11-13T07:26:03Z |
| format | Article |
| id | oai:scholars.utp.edu.my:1731 |
| institution | Universiti Teknologi Petronas |
| institution_category | Local University |
| last_indexed | 2025-11-13T07:26:03Z |
| publishDate | 2009 |
| publisher | World Academy of Science, Engineering and Technology (WASET) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:scholars.utp.edu.my:17312010-04-27T07:25:23Z http://scholars.utp.edu.my/id/eprint/1731/ Hand Gesture Recognition: Sign to Voice System (S2V) Foong, Oi Mean QA75 Electronic computers. Computer science Hand gesture is one of the typical methods used in sign language for non-verbal communication. It is most commonly used by people who have hearing or speech problems to communicate among themselves or with normal people. Various sign language systems have been developed by manufacturers around the globe but they are neither flexible nor cost-effective for the end users. This paper presents a system prototype that is able to automatically recognize sign language to help normal people to communicate more effectively with the hearing or speech impaired people. The Sign to Voice system prototype, S2V, was developed using Feed Forward Neural Network for two-sequence signs detection. Different sets of universal hand gestures were captured from video camera and utilized to train the neural network for classification purpose. The experimental results have shown that neural network has achieved satisfactory result for sign-to-voice translation. World Academy of Science, Engineering and Technology (WASET) 2009-09 Article PeerReviewed Foong, Oi Mean (2009) Hand Gesture Recognition: Sign to Voice System (S2V). International Journal of Electrical, Computer and Systems Engineering (IJECSE), 3 (4). pp. 198-202. ISSN 2070-3813 http://www.waset.org/journals/ijecse/v3/v3-4-33.pdf |
| spellingShingle | QA75 Electronic computers. Computer science Foong, Oi Mean Hand Gesture Recognition: Sign to Voice System (S2V) |
| title | Hand Gesture Recognition: Sign to Voice System (S2V)
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| title_full | Hand Gesture Recognition: Sign to Voice System (S2V)
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| title_fullStr | Hand Gesture Recognition: Sign to Voice System (S2V)
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| title_full_unstemmed | Hand Gesture Recognition: Sign to Voice System (S2V)
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| title_short | Hand Gesture Recognition: Sign to Voice System (S2V)
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| title_sort | hand gesture recognition: sign to voice system (s2v) |
| topic | QA75 Electronic computers. Computer science |
| url | http://scholars.utp.edu.my/id/eprint/1731/ http://scholars.utp.edu.my/id/eprint/1731/ |