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

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.

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Abstract

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.

Metadata

Item Type: Book Section
Creators:
Creators
Email
Mohd Jalani, Nurul Natasha
nurulnmj98@gmail.com
Zamzuri, Zainal Fikri
UNSPECIFIED
Contributors:
Contribution
Name
Email / ID Num.
Patron
Mohd Nor, Abd Halim
UNSPECIFIED
Advisor
Md Badarudin, Ismadi
UNSPECIFIED
Advisor
Jono, Nor Hajar Hasrol
UNSPECIFIED
Advisor
Ismail, Shafinar
UNSPECIFIED
Advisor
Maulan, Sumarni
UNSPECIFIED
Advisor
Md Yusuf, Ahmad Harith Syah
UNSPECIFIED
Advisor
Mahpoth, Halim
UNSPECIFIED
Subjects: P Language and Literature > P Philology. Linguistics > Language. Linguistic theory. Comparative grammar > Philosophy, origin, etc. of language > Sign language. Gesture
Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science
Divisions: Universiti Teknologi MARA, Melaka > Alor Gajah Campus
Item ID: 46006
Uncontrolled Keywords: Sign language; Mobile application; Convolutional Neural Network; CNN; Modified Waterfall Model
URI: https://ir.uitm.edu.my/id/eprint/46006

ID Number

46006

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