Malaysian Sign Language recognition using LSTM / Nurul Amila Ilyana Mohamad Nizam

This study addresses the communication challenges faced by the deaf and hard of hearing community in Malaysia due to the scarcity of Malaysian Sign Language (MSL) interpreters. MSL, as the official sign language of the country, plays a crucial role in facilitating communication for individuals with...

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Main Author:	Mohamad Nizam, Nurul Amila Ilyana
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/96519/1/96519.pdf
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id	my-uitm-ir.96519
record_format	uketd_dc
spelling	my-uitm-ir.965192024-06-06T06:38:03Z Malaysian Sign Language recognition using LSTM / Nurul Amila Ilyana Mohamad Nizam 2024 Mohamad Nizam, Nurul Amila Ilyana Neural networks (Computer science) This study addresses the communication challenges faced by the deaf and hard of hearing community in Malaysia due to the scarcity of Malaysian Sign Language (MSL) interpreters. MSL, as the official sign language of the country, plays a crucial role in facilitating communication for individuals with hearing impairments. However, the limited availability of interpreters hinders effective communication. In response, this paper introduces an innovative approach utilizing Long Short-Term Memory (LSTM) neural networks for the recognition of Malaysian Sign Language. LSTM networks, recognized for their proficiency in sequence modeling tasks, are employed to discern the temporal correlations inherent in the hand movements constituting MSL signs. The proposed method is rigorously evaluated using a dataset comprised of MSL signs, incorporating metrics such as accuracy and confusion matrix analysis. The results demonstrate a notable precision in sign recognition, reinforcing the potential of the method to form the foundation for a real-time MSL recognition system. By leveraging the capabilities of LSTM networks, this research contributes to enhancing accessibility and communication for the deaf and hard of hearing community in Malaysia. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96519/ https://ir.uitm.edu.my/id/eprint/96519/1/96519.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Othman, Ahmad Kamarulzaman
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Othman, Ahmad Kamarulzaman
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Mohamad Nizam, Nurul Amila Ilyana Malaysian Sign Language recognition using LSTM / Nurul Amila Ilyana Mohamad Nizam
description	This study addresses the communication challenges faced by the deaf and hard of hearing community in Malaysia due to the scarcity of Malaysian Sign Language (MSL) interpreters. MSL, as the official sign language of the country, plays a crucial role in facilitating communication for individuals with hearing impairments. However, the limited availability of interpreters hinders effective communication. In response, this paper introduces an innovative approach utilizing Long Short-Term Memory (LSTM) neural networks for the recognition of Malaysian Sign Language. LSTM networks, recognized for their proficiency in sequence modeling tasks, are employed to discern the temporal correlations inherent in the hand movements constituting MSL signs. The proposed method is rigorously evaluated using a dataset comprised of MSL signs, incorporating metrics such as accuracy and confusion matrix analysis. The results demonstrate a notable precision in sign recognition, reinforcing the potential of the method to form the foundation for a real-time MSL recognition system. By leveraging the capabilities of LSTM networks, this research contributes to enhancing accessibility and communication for the deaf and hard of hearing community in Malaysia.
format	Thesis
qualification_level	Bachelor degree
author	Mohamad Nizam, Nurul Amila Ilyana
author_facet	Mohamad Nizam, Nurul Amila Ilyana
author_sort	Mohamad Nizam, Nurul Amila Ilyana
title	Malaysian Sign Language recognition using LSTM / Nurul Amila Ilyana Mohamad Nizam
title_short	Malaysian Sign Language recognition using LSTM / Nurul Amila Ilyana Mohamad Nizam
title_full	Malaysian Sign Language recognition using LSTM / Nurul Amila Ilyana Mohamad Nizam
title_fullStr	Malaysian Sign Language recognition using LSTM / Nurul Amila Ilyana Mohamad Nizam
title_full_unstemmed	Malaysian Sign Language recognition using LSTM / Nurul Amila Ilyana Mohamad Nizam
title_sort	malaysian sign language recognition using lstm / nurul amila ilyana mohamad nizam
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96519/1/96519.pdf
_version_	1804889992021934080

Malaysian Sign Language recognition using LSTM / Nurul Amila Ilyana Mohamad Nizam

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