Sign Language Translator Using KNN
DOI:
https://doi.org/10.17010/ijcs/2024/v9/i3/174149Keywords:
KNN
, Sign Language Translator.Paper Submission Date
, April 18, 2024, Paper sent back for Revision, April 30, Paper Acceptance Date, May 5, Paper Published Online, June 5, 2024.Abstract
Sign language is a profoundly rich and expressive way to communicate, and it is vital for the deaf and hard-of- hearing communities. It provides a distinct avenue for sharing thoughts, feelings, and ideas, helping these individuals to connect and share their viewpoints. Nevertheless, the digital divide remains a significant hurdle, limiting their ability to communicate effectively and integrate fully into a predominantly spoken and written linguistic environment. Our initiative serves as a ray of hope in a time when technology has the potential to close these gaps and promote inclusivity. We are committed to creating a sign language translator powered by Machine Learning. Our main objective is to deliver a real-time translation tool that overcomes these obstacles, facilitating seamless communication between the deaf community and the wider hearing population. By leveraging the power of modern technology, we strive to empower deaf and hard-of-hearing people, enabling them to fully participate in society, access information, and express themselves naturally and inclusively. Our project is a crucial step toward making the world more accessible and fair for everyone.
Downloads
Published
How to Cite
Issue
Section
References
M. Papatsimouli, K.-F. Kollias, L. Lazaridis, G. Maraslidis, H. Michailidis, P. Sarigiannidis, and G. F. Fragulis, “Real time sign language translation systems: A review study,†in 2022 11th Int. Conf. Modern Circuits Syst. Technologies, Bremen, Germany, pp. 1–4, 2022, doi: 10.1109/MO- CAST54814.2022.9837666.
C. O. Sosa-Jimenez, H. V. Rıos-Figueroa, and A. L. Solıs-Gonzalez- Cosıo, “A prototype for Mexican sign language recognition and synthesis in support of a primary care physician,†in IEEE Access, vol. 10, pp. 127620–127635, 2022, doi: 10.1109/ACCESS.2022.3226696.
A. Taokeer and R. Murugan, “Sign language detector using cloud,†Int. J. Trend Scientific Res. Dev., vol. 6, no.3, pp. 1282–1286, 2022. [Online]. Available: https://www.ijtsrd.com/papers/ijtsrd49698.pdf
S. Johnny, S. J. Nirmala, “Sign language translator using Machine Learning,†SN Comput. Sci, vol. 3, Art. no. 36, 2022, doi: 10.1007/s42979- 021-00896-y.
H. P. Martınez, G. Y. Yannakakis, and N. Yannakakis, “Deep multimodal fusion: Combining discrete events and continuous signals,†in ICMI '14: Proc. 16th Int. Conf. Multimodal Interaction Proc. 16th Int. Conf. Multimodal Interaction, Nov. 2014, pp. 34–41, doi: 10.1145/2663204.2663236.
H. D. Patel and A. Saluja, “Sign language recognition and translator application,†Int. Res. J. Eng. Technol., vol. 8, no. 9, pp. 2395–0056, Sep. 2021. [Online]. Available: https://www.irjet.net/archives/V8/i9/IRJET-V8I9100.pdf
B. Mangesh, K. Mayur, and P. Rujali, “Sign language text to speech converter using image processing and CNN,†Int. Res. J. Eng. Technol., vol. 7, no. 4., Apr. 2020.
[Online]. Available: https://www.irjet.net/archives/V7/i4/IRJET-V7I4126.pdf
R. A. Isaac and S. S. Gayathri, “Sign language interpreter,†Int. Res. J. Eng. Technol., vol. 5, no. 10, pp. 248–251, Oct. 2018.