Silent Signals : AI Power Sign Language Recognization

Pratik, Kapale and Piyush, Kuthe and Kunal, Kohale and Pranay, Bawanthade and Prof. Suman, Sengupta (2024) Silent Signals : AI Power Sign Language Recognization. International Journal of Trend in Scientific Research and Development, 8 (5). pp. 321-328. ISSN 2456-6470

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Abstract

Sign language recognition plays a crucial role in bridging the communication gap between the hearing-impaired community and the rest of society. This project focuses on developing a robust system that can accurately recognize and interpret sign language gestures into corresponding text or speech, leveraging computer vision and machine learning techniques. By utilizing deep learning models, particularly convolutional neural networks (CNNs) for image classification and recurrent neural networks (RNNs) for sequential gesture recognition, the system aims to achieve real-time performance. The proposed system uses a camera to capture hand gestures and processes them to identify individual signs and dynamic sequences, handling variations in lighting, backgrounds, and individual signers. Our model is trained on a diverse dataset of sign language gestures to ensure its adaptability across different users and environments. The ultimate goal of the project is to create a reliable tool that enhances accessibility and fosters more inclusive communication for the deaf and hard-of-hearing communities.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	25 Oct 2024 05:18
Last Modified:	25 Oct 2024 05:18
URI:	http://eprints.umsida.ac.id/id/eprint/14392

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