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iMalaySign: Malaysian sign language recognition mobile application using Convolutional Neural Network (CNN) / Nurul Natasha Mohd Jalani and Zainal Fikri Zamzuri

Sign language is used as one of the medium interactions among the deaf community in all around the world. Therefore, in Malaysia we used Malaysian sign language for deaf community to use in order to interact with people. Alphabets of Malaysian sign language also used as to spell some words, names, o...

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Main Authors: Mohd Jalani, Nurul Natasha, Zamzuri, Zainal Fikri
Format: Book Section
Language:English
Published: Akademi Pengajian Bahasa 2021
Subjects:
Sign language. Gesture
Electronic Computers. Computer Science
Online Access:http://ir.uitm.edu.my/id/eprint/46006/1/46006.pdf
http://ir.uitm.edu.my/id/eprint/46006/
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spelling my.uitm.ir.460062021-06-07T08:03:19Z http://ir.uitm.edu.my/id/eprint/46006/ iMalaySign: Malaysian sign language recognition mobile application using Convolutional Neural Network (CNN) / Nurul Natasha Mohd Jalani and Zainal Fikri Zamzuri Mohd Jalani, Nurul Natasha Zamzuri, Zainal Fikri Sign language. Gesture Electronic Computers. Computer Science Sign language is used as one of the medium interactions among the deaf community in all around the world. Therefore, in Malaysia we used Malaysian sign language for deaf community to use in order to interact with people. Alphabets of Malaysian sign language also used as to spell some words, names, or any sign. However, sign language is only known by deaf people or people who learn sign language. Due to this, communication will not be delivered to anyone who do not understand or know sign language. This is due to the reason of people think sign language is hard to learn and sign language takes time to be learn. To add, some people for some reason had refused to learn sign language. Thus, a mobile application of Malaysian sign language recognition can be delivered to help deaf people communicate with normal people in our society. The scope of this research is focusing on five alphabets which covered alphabet (A-E). The objectives of this research as to design a mobile application that can help people knowing the meaning of alphabets, to develop this project by using image recognition technique and evaluate the functionality and accuracy of the sign language pose through the application. The methodology used in this application development is Modified Waterfall Model and CNN technique as the image recognition technique to recognize the alphabets pose. This application is tested with functionality and accuracy testing. As a result, this application is well function without any errors and can recognize the alphabet poses with a good accuracy. For future research is recommended to improve this application by increase the scope of the Malaysian sign language together with recognizing movement in Malaysian sign language. Akademi Pengajian Bahasa 2021 Book Section PeerReviewed text en http://ir.uitm.edu.my/id/eprint/46006/1/46006.pdf ID46006 Mohd Jalani, Nurul Natasha and Zamzuri, Zainal Fikri (2021) iMalaySign: Malaysian sign language recognition mobile application using Convolutional Neural Network (CNN) / Nurul Natasha Mohd Jalani and Zainal Fikri Zamzuri. In: International Conference of Research on Language Education (I–Role) 2021: Engaging in Change: Empowering Linguistics, Literature & Language. Akademi Pengajian Bahasa, Alor Gajah, Melaka.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Sign language. Gesture
Electronic Computers. Computer Science
spellingShingle Sign language. Gesture
Electronic Computers. Computer Science
Mohd Jalani, Nurul Natasha
Zamzuri, Zainal Fikri
iMalaySign: Malaysian sign language recognition mobile application using Convolutional Neural Network (CNN) / Nurul Natasha Mohd Jalani and Zainal Fikri Zamzuri
description Sign language is used as one of the medium interactions among the deaf community in all around the world. Therefore, in Malaysia we used Malaysian sign language for deaf community to use in order to interact with people. Alphabets of Malaysian sign language also used as to spell some words, names, or any sign. However, sign language is only known by deaf people or people who learn sign language. Due to this, communication will not be delivered to anyone who do not understand or know sign language. This is due to the reason of people think sign language is hard to learn and sign language takes time to be learn. To add, some people for some reason had refused to learn sign language. Thus, a mobile application of Malaysian sign language recognition can be delivered to help deaf people communicate with normal people in our society. The scope of this research is focusing on five alphabets which covered alphabet (A-E). The objectives of this research as to design a mobile application that can help people knowing the meaning of alphabets, to develop this project by using image recognition technique and evaluate the functionality and accuracy of the sign language pose through the application. The methodology used in this application development is Modified Waterfall Model and CNN technique as the image recognition technique to recognize the alphabets pose. This application is tested with functionality and accuracy testing. As a result, this application is well function without any errors and can recognize the alphabet poses with a good accuracy. For future research is recommended to improve this application by increase the scope of the Malaysian sign language together with recognizing movement in Malaysian sign language.
format Book Section
author Mohd Jalani, Nurul Natasha
Zamzuri, Zainal Fikri
author_facet Mohd Jalani, Nurul Natasha
Zamzuri, Zainal Fikri
author_sort Mohd Jalani, Nurul Natasha
title iMalaySign: Malaysian sign language recognition mobile application using Convolutional Neural Network (CNN) / Nurul Natasha Mohd Jalani and Zainal Fikri Zamzuri
title_short iMalaySign: Malaysian sign language recognition mobile application using Convolutional Neural Network (CNN) / Nurul Natasha Mohd Jalani and Zainal Fikri Zamzuri
title_full iMalaySign: Malaysian sign language recognition mobile application using Convolutional Neural Network (CNN) / Nurul Natasha Mohd Jalani and Zainal Fikri Zamzuri
title_fullStr iMalaySign: Malaysian sign language recognition mobile application using Convolutional Neural Network (CNN) / Nurul Natasha Mohd Jalani and Zainal Fikri Zamzuri
title_full_unstemmed iMalaySign: Malaysian sign language recognition mobile application using Convolutional Neural Network (CNN) / Nurul Natasha Mohd Jalani and Zainal Fikri Zamzuri
title_sort imalaysign: malaysian sign language recognition mobile application using convolutional neural network (cnn) / nurul natasha mohd jalani and zainal fikri zamzuri
publisher Akademi Pengajian Bahasa
publishDate 2021
url http://ir.uitm.edu.my/id/eprint/46006/1/46006.pdf
http://ir.uitm.edu.my/id/eprint/46006/
_version_ 1702172617490825216
score 13.211869

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