Real-Time Sign Language Conversion Using CNN Algorithm for Disabled People

Authors

  • Disha Deotale Student, Department of Computer Engineering, G. H. Raisoni College of Engineering and Management, Pune, India
  • Harshada Patil Student, Department of Computer Engineering, G. H. Raisoni College of Engineering and Management, Pune, India
  • Sanghavi Appam Student, Department of Computer Engineering, G. H. Raisoni College of Engineering and Management, Pune, India
  • Vaibhavi Padamwar Student, Department of Computer Engineering, G. H. Raisoni College of Engineering and Management, Pune, India
  • Sandeep Malviya Student, Department of Computer Engineering, G. H. Raisoni College of Engineering and Management, Pune, India

Keywords:

CNN, Gesture recognition, Sign language

Abstract

Various problems in speaking and hearing put a lot of impact on one’s daily life and professional growth. Thus, Sign Language (SL) is considered as the most effective solution to people with hearing and speech impairment. Sign language is not understandable by everyone. Hence, there is a need to understand sign language in verbal form with the help of converting hand gestures into sentences. Advanced methods like Machine Learning and Deep learning provides many innovative solutions to identify hand gestures.

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Published

03-06-2022

Issue

Vol. 5 No. 5 (2022)

Section

Articles

License

Copyright (c) 2022 Disha Deotale, Harshada Patil, Sanghavi Appam, Vaibhavi Padamwar, Sandeep Malviya

Creative Commons License

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

How to Cite

[1]
D. Deotale, H. Patil, S. Appam, V. Padamwar, and S. Malviya, “Real-Time Sign Language Conversion Using CNN Algorithm for Disabled People”, IJRESM, vol. 5, no. 5, pp. 260–262, Jun. 2022, Accessed: Apr. 27, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/2120