Version 1
: Received: 23 May 2024 / Approved: 24 May 2024 / Online: 24 May 2024 (08:38:34 CEST)
How to cite:
Basheer, R.; Aziz, M. A. A.; Arifa, H. Convolutional Neural Network Based Malaysian Sign-Language Recognition Web Application. Preprints2024, 2024051601. https://doi.org/10.20944/preprints202405.1601.v1
Basheer, R.; Aziz, M. A. A.; Arifa, H. Convolutional Neural Network Based Malaysian Sign-Language Recognition Web Application. Preprints 2024, 2024051601. https://doi.org/10.20944/preprints202405.1601.v1
Basheer, R.; Aziz, M. A. A.; Arifa, H. Convolutional Neural Network Based Malaysian Sign-Language Recognition Web Application. Preprints2024, 2024051601. https://doi.org/10.20944/preprints202405.1601.v1
APA Style
Basheer, R., Aziz, M. A. A., & Arifa, H. (2024). Convolutional Neural Network Based Malaysian Sign-Language Recognition Web Application. Preprints. https://doi.org/10.20944/preprints202405.1601.v1
Chicago/Turabian Style
Basheer, R., Mochamad Azkal Azkiya Aziz and Habiba Arifa. 2024 "Convolutional Neural Network Based Malaysian Sign-Language Recognition Web Application" Preprints. https://doi.org/10.20944/preprints202405.1601.v1
Abstract
The deaf-mute and individuals with hearing disabilities who communicate through sign language should not be overlooked or excluded. It is a social responsibility to strive for an inclusive world without inequality. Advances in technology offer various solutions to address the communication gap, one of which is the use of computer-based sign language recognition systems. This project proposes a Malaysian Sign Language recognition model utilizing MobileNetV1, a convolutional neural network-based algorithm, to interpret sign language in real-time settings through a website application using Streamlit. The outcome of the model test shows that it has achieved a recognition rate of 96 percent. Although the proposed model has demonstrated the ability to recognize sign language, it is suggested that further improvement can be achieved by enhancing the quality and diversity of the dataset. The implementation of a more comprehensive dataset would lead to improved performance and increased accuracy of the model. This project underscores the importance of considering and accommodating the needs of the deaf-mute and individuals with hearing disabilities and highlights the potential of technology to bridge communication barriers and achieve a more inclusive society.
Keywords
CONVOLUTIONAL NEURAL NETWORK; MALAYSIAN SIGN-LANGUAGE; RECOGNITION WEB APPLICATION
Subject
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
