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Sign language detection using convolutional neural network for teaching and learning application

Teaching lower school mathematic could be easy for everyone. For teaching in the situation that cannot speak, using sign language is the answer especially someone that have infected with vocal cord infection or critical spasmodic dysphonia or maybe disable people. However, the situation could be dif...

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Main Authors: Wan Bejuri, Wan Mohd Ya'akob, Syed Ahmad, Sharifah Sakinah, Zakaria, Nur’Ain Najiha, S. M. M Yassin, S. M. Warusia Mohamed, Ngo, Hea Choon, Mohamad, Mohd Murtadha
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26210/2/SIGN_LANGUAGE_DETECTION_USING_CONVOLUTIONAL_NEURAL.PDF
http://eprints.utem.edu.my/id/eprint/26210/
https://ijeecs.iaescore.com/index.php/IJEECS/article/view/27423/16714
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spelling my.utem.eprints.262102023-03-02T12:06:51Z http://eprints.utem.edu.my/id/eprint/26210/ Sign language detection using convolutional neural network for teaching and learning application Wan Bejuri, Wan Mohd Ya'akob Syed Ahmad, Sharifah Sakinah Zakaria, Nur’Ain Najiha S. M. M Yassin, S. M. Warusia Mohamed Ngo, Hea Choon Mohamad, Mohd Murtadha Teaching lower school mathematic could be easy for everyone. For teaching in the situation that cannot speak, using sign language is the answer especially someone that have infected with vocal cord infection or critical spasmodic dysphonia or maybe disable people. However, the situation could be difficult, when the sign language is not understandable by the audience. Thus, the purpose of this research is to design a sign language detection scheme for teaching and learning activity. In this research, the image of hand gestures from teacher or presenter will be taken by using a web camera for the system to anticipate and display the image's name. This proposed scheme will detects hand movements and convert it be meaningful information. As a result, it show the model can be the most consistent in term of accuracy and loss compared to others method. Furthermore, the proposed algorithm is expected to contribute the body of knowledge and the society. Institute of Advanced Engineering and Science 2022-10 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26210/2/SIGN_LANGUAGE_DETECTION_USING_CONVOLUTIONAL_NEURAL.PDF Wan Bejuri, Wan Mohd Ya'akob and Syed Ahmad, Sharifah Sakinah and Zakaria, Nur’Ain Najiha and S. M. M Yassin, S. M. Warusia Mohamed and Ngo, Hea Choon and Mohamad, Mohd Murtadha (2022) Sign language detection using convolutional neural network for teaching and learning application. Indonesian Journal of Electrical Engineering and Computer Science, 28 (1). pp. 358-364. ISSN 2502-4752 https://ijeecs.iaescore.com/index.php/IJEECS/article/view/27423/16714 10.11591/ijeecs.v28.i1.pp358-364
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Teaching lower school mathematic could be easy for everyone. For teaching in the situation that cannot speak, using sign language is the answer especially someone that have infected with vocal cord infection or critical spasmodic dysphonia or maybe disable people. However, the situation could be difficult, when the sign language is not understandable by the audience. Thus, the purpose of this research is to design a sign language detection scheme for teaching and learning activity. In this research, the image of hand gestures from teacher or presenter will be taken by using a web camera for the system to anticipate and display the image's name. This proposed scheme will detects hand movements and convert it be meaningful information. As a result, it show the model can be the most consistent in term of accuracy and loss compared to others method. Furthermore, the proposed algorithm is expected to contribute the body of knowledge and the society.
format Article
author Wan Bejuri, Wan Mohd Ya'akob
Syed Ahmad, Sharifah Sakinah
Zakaria, Nur’Ain Najiha
S. M. M Yassin, S. M. Warusia Mohamed
Ngo, Hea Choon
Mohamad, Mohd Murtadha
spellingShingle Wan Bejuri, Wan Mohd Ya'akob
Syed Ahmad, Sharifah Sakinah
Zakaria, Nur’Ain Najiha
S. M. M Yassin, S. M. Warusia Mohamed
Ngo, Hea Choon
Mohamad, Mohd Murtadha
Sign language detection using convolutional neural network for teaching and learning application
author_facet Wan Bejuri, Wan Mohd Ya'akob
Syed Ahmad, Sharifah Sakinah
Zakaria, Nur’Ain Najiha
S. M. M Yassin, S. M. Warusia Mohamed
Ngo, Hea Choon
Mohamad, Mohd Murtadha
author_sort Wan Bejuri, Wan Mohd Ya'akob
title Sign language detection using convolutional neural network for teaching and learning application
title_short Sign language detection using convolutional neural network for teaching and learning application
title_full Sign language detection using convolutional neural network for teaching and learning application
title_fullStr Sign language detection using convolutional neural network for teaching and learning application
title_full_unstemmed Sign language detection using convolutional neural network for teaching and learning application
title_sort sign language detection using convolutional neural network for teaching and learning application
publisher Institute of Advanced Engineering and Science
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26210/2/SIGN_LANGUAGE_DETECTION_USING_CONVOLUTIONAL_NEURAL.PDF
http://eprints.utem.edu.my/id/eprint/26210/
https://ijeecs.iaescore.com/index.php/IJEECS/article/view/27423/16714
_version_ 1759693056119930880
score 13.223943

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