SignaTalk: Machine Learning –Powered Real-time Sign Language interpretation

Ranjitha Bai A; Harshitha D; Maruti Biradi; Yashashwini V

SignaTalk: Machine Learning –Powered Real-time Sign Language interpretation

May 2024

Vol-10, Issue-3

Paper ID: 24037

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Information Science and Engineering

Keywords

sign language detection real-time hand tracking classification computer vision machine learning accessibility .

Abstract

The creation of a real-time system for detecting sign language is essential to promoting inclusiveness and successful communication for people with hearing impairments. This initiative tackles a major obstacle that the deaf and hard-of-hearing community has by offering a way to read and comprehend sign language motions in real-time. We provide a thorough analysis of our real-time system for detecting sign language in this research, which integrates classification and hand tracking methods. High confidence levels and real-time responsiveness are shown in the results, indicating that it is a viable option for improving communication between non-sign language users and those with hearing difficulties. The suggested method has a lot of potential for use in professional, social, and educational contexts, which will eventually lead to a society that is more accessible and fair.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	Ranjitha Bai A	Vidya Vikas Institute of Engineering and Technology
2	Harshitha D	Vidya Vikas Institute of Engineering and Technology
3	Maruti Biradi	Vidya Vikas Institute of Engineering and Technology
4	Yashashwini V	Vidya Vikas Institute of Engineering and Technology

How to Cite

Use the following formats to cite this article in your research.

APA Style

A, Ranjitha Bai, D, Harshitha, Biradi, Maruti, & V, Yashashwini (2024). SignaTalk: Machine Learning –Powered Real-time Sign Language interpretation. International Journal of Advance Research and Innovative Ideas In Education, 10(3), 2745-2752.

MLA Style

A, Ranjitha Bai, et al. "SignaTalk: Machine Learning –Powered Real-time Sign Language interpretation." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 3, 2024, pp. 2745-2752.

IEEE Style

Ranjitha Bai A, Harshitha D, Maruti Biradi, and Yashashwini V, "SignaTalk: Machine Learning –Powered Real-time Sign Language interpretation," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 3, pp. 2745-2752, 2024.

Vancouver Style

A Ranjitha Bai, D Harshitha, Biradi Maruti, V Yashashwini. SignaTalk: Machine Learning –Powered Real-time Sign Language interpretation. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(3):2745-2752.

Harvard Style

A, Ranjitha Bai, D, Harshitha, Biradi, Maruti, & V, Yashashwini (2024) 'SignaTalk: Machine Learning –Powered Real-time Sign Language interpretation', International Journal of Advance Research and Innovative Ideas In Education, 10(3), pp. 2745-2752.

Chicago Style

A, Ranjitha Bai, et al. "SignaTalk: Machine Learning –Powered Real-time Sign Language interpretation." International Journal of Advance Research and Innovative Ideas In Education 10, no. 3 (2024): 2745-2752.

Turabian Style

A, Ranjitha Bai, et al. "SignaTalk: Machine Learning –Powered Real-time Sign Language interpretation." International Journal of Advance Research and Innovative Ideas In Education 10, no. 3 (2024): 2745-2752.