Real-time Malaysian sign language translation using colour segmentation and neural network
In this paper we present an automatic visual-based sign language translation system. Our proposed automatic sign-language translator provides a real-time English translation of the Malaysia SL. To date, there have been studies on sign language recognition based on visual approach (video camera). Ho...
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iium-53842012-05-23T01:30:59Z http://irep.iium.edu.my/5384/ Real-time Malaysian sign language translation using colour segmentation and neural network Akmeliawati, Rini Melanie, Po-Leen Ooi Ye, Chow Kuang TK Electrical engineering. Electronics Nuclear engineering In this paper we present an automatic visual-based sign language translation system. Our proposed automatic sign-language translator provides a real-time English translation of the Malaysia SL. To date, there have been studies on sign language recognition based on visual approach (video camera). However, the emphasis on these works is limited to a small lexicon of sign language or solely focuses on fingerspelling, which takes diferent approaches respectively. In practical sense, fingerspelling is used if a word cannot be expressed via sign language. Our sign language translator can recognise both fingerspelling and sign gestures that involve static and motion signs. Trained neural networks are used to identify the signs to translate into English. 2007 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/5384/1/imtc2007_signlanguage.pdf Akmeliawati, Rini and Melanie, Po-Leen Ooi and Ye, Chow Kuang (2007) Real-time Malaysian sign language translation using colour segmentation and neural network. In: IMTC 2007 - Instrumentation and Measurement Technology Conference Warsaw, Poland, 1-3 May 2007, 1-3 May 2007 , Warsaw, Poland . http://dx.doi.org/10.1109/IMTC.2007.379311 doi:10.1109/IMTC.2007.379311 |
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institution |
International Islamic University Malaysia |
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Online Access |
language |
English |
topic |
TK Electrical engineering. Electronics Nuclear engineering |
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TK Electrical engineering. Electronics Nuclear engineering Akmeliawati, Rini Melanie, Po-Leen Ooi Ye, Chow Kuang Real-time Malaysian sign language translation using colour segmentation and neural network |
description |
In this paper we present an automatic visual-based sign
language translation system. Our proposed automatic sign-language translator provides a real-time English translation of the Malaysia SL. To date, there have been studies on sign language recognition based on visual approach (video camera). However, the emphasis on these works is limited to a small lexicon of sign language or
solely focuses on fingerspelling, which takes diferent approaches respectively. In practical sense, fingerspelling is used if a word cannot be expressed via sign language. Our sign language translator can recognise both fingerspelling and sign gestures that involve static and motion signs. Trained neural networks are used to identify the signs to translate into English. |
format |
Conference or Workshop Item |
author |
Akmeliawati, Rini Melanie, Po-Leen Ooi Ye, Chow Kuang |
author_facet |
Akmeliawati, Rini Melanie, Po-Leen Ooi Ye, Chow Kuang |
author_sort |
Akmeliawati, Rini |
title |
Real-time Malaysian sign language translation using colour segmentation and neural network |
title_short |
Real-time Malaysian sign language translation using colour segmentation and neural network |
title_full |
Real-time Malaysian sign language translation using colour segmentation and neural network |
title_fullStr |
Real-time Malaysian sign language translation using colour segmentation and neural network |
title_full_unstemmed |
Real-time Malaysian sign language translation using colour segmentation and neural network |
title_sort |
real-time malaysian sign language translation using colour segmentation and neural network |
publishDate |
2007 |
url |
http://irep.iium.edu.my/5384/ http://irep.iium.edu.my/5384/ http://irep.iium.edu.my/5384/ http://irep.iium.edu.my/5384/1/imtc2007_signlanguage.pdf |
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2023-09-18T20:13:57Z |
last_indexed |
2023-09-18T20:13:57Z |
_version_ |
1777407662722383872 |