Sign Language Recognition and Video Generation Using Deep Learning

Authors

  • Meera Treesa Mathews Department of Computer Science and Engineering, Federal Institute of Science and Technology, Angamaly, Kerala, India
  • Joyal Raphel Department of Computer Science and Engineering, Federal Institute of Science and Technology, Angamaly, Kerala, India
  • Joseph Shaju C Department of Computer Science and Engineering, Federal Institute of Science and Technology, Angamaly, Kerala, India
  • Steve Soney Varghese Department of Computer Science and Engineering, Federal Institute of Science and Technology, Angamaly, Kerala, India
  • Paul J Puthusserry Department of Computer Science and Engineering, Federal Institute of Science and Technology, Angamaly, Kerala, India

DOI:

https://doi.org/10.61779/jasetm.v1i2.4

Keywords:

Gesture recognition, Deep Learning, Sign language, Video generation

Abstract

The proposed system aims to help normal people understand the communication of speech impaired individuals through hand gestures recognition and generating animation gestures. The system focuses on recognizing different hand gestures and converting them into information that is understandable by normal people. YOLOv8 model, a state-of-the-art object detection algorithm, is being employed in this system to detect and classify sign language gestures. Sign language video generation can act as a guide for anyone who is in the process of learning sign language, by providing them with expressive sign language videos using avatars that can translate the user inputs to sign language videos. CWASA Package and SiGML files are used for this process. The project contributes to the advancement of assistive technologies for the hearing-impaired community, offering innovative solutions for sign language recognition and video generation.

Published

02-12-2023

How to Cite

Mathews, M. T. ., Raphel, J., Shaju C, J., Varghese, S. S. ., & Puthusserry, P. J. (2023). Sign Language Recognition and Video Generation Using Deep Learning. Journal of Applied Science, Engineering, Technology and Management, 1(02), 13–16. https://doi.org/10.61779/jasetm.v1i2.4

Issue

Vol. 1 No. 02 (2023): Journal of Applied Science, Engineering, Technology and Management

Section

Articles

License

Copyright (c) 2023 Meera Treesa Mathews, Joyal Raphel, Joseph Shaju C, Steve Soney Varghese, Paul J Puthusserry

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