Sign Language Recognition Using Machine Learning


Authors : Dr. Bhuvaneshwari K V; Bindu A R; Manvitha G K; Nikitha N Chinchali; Nisha K N

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/yeyxv5s7

Scribd : https://tinyurl.com/493wu8ww

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY273

Abstract : Communication is very important in human daily life and the most widely used type of communication is verbal communication. But there are people with hearing and speech impairment who cannot communicate verbally and the language which they use for communication is sign language. Many other languages, tools are being developed for inter-language translation from sign language to text. There has been a lot of research done in the field of American Sign Language but the work is limited in the case of Indian Sign Language. This is due to lack of standards and the variation in the language. The proposed system aims to recognize Indian Sign Language digit gestures and convert it into text. By using Machine Learning Techniques, sign language recognition leads to the development of a more accurate and robust system. As Deep learning techniques, ResNet100 and ensemble models continue to evolve, sign language recognition system plays a transformative role in bridging the communication gap between deaf and hearing individuals. It helps the user to recognize the sign language by using this proposed system.

Keywords : Sign Language, Convolutional Neural Networks, Residual Network , Random Forest Classifier, Ensemble Model.

References :

  1. Sakshi Sharma,Sukhwinder Singh, “Vision-based sign language recognition system: A Comprehensive Review” Published in 2020 International Conference on Inventive Computation Technologies (ICICT) published on June 2020.
  2. D Sathyanarayanan, T. Srinivasa Reddy, A. Sathish, P. Geetha, J.R. Arunkumar, S. Prem Kumar Deepak, "American Sign Language Recognition System for Numerical and Alphabets", 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), pp.1-6, 2023.
  3. M. Alfonse, A. Ali, A. S. Elons, N. L. Badr and M. Aboul-Ela, "Arabic sign language benchmark database for different heterogeneous sensors", Proc. 5th Int. Conf. Inf. Commun. Technol. Accessibility (ICTA), pp. 1-9, Dec. 2016.
  4. M. E. R. Grif and A. B. M. R. E. Prikhodko, ‘‘Recognition of Russian and Indian sign languages based on machine learning,’’ Anal. Data Process. Syst., vol. 3, no. 83, pp. 53–74, 2021.
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  11. A. K. Sahoo, G. S. Mishra, K. K. Ravulakollu, ARPN J. Eng. Appl. Sci. 2014, 9, 116. “Indian sign language recognition using ensemble based classifier combination”, Feb. 2022
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Communication is very important in human daily life and the most widely used type of communication is verbal communication. But there are people with hearing and speech impairment who cannot communicate verbally and the language which they use for communication is sign language. Many other languages, tools are being developed for inter-language translation from sign language to text. There has been a lot of research done in the field of American Sign Language but the work is limited in the case of Indian Sign Language. This is due to lack of standards and the variation in the language. The proposed system aims to recognize Indian Sign Language digit gestures and convert it into text. By using Machine Learning Techniques, sign language recognition leads to the development of a more accurate and robust system. As Deep learning techniques, ResNet100 and ensemble models continue to evolve, sign language recognition system plays a transformative role in bridging the communication gap between deaf and hearing individuals. It helps the user to recognize the sign language by using this proposed system.

Keywords : Sign Language, Convolutional Neural Networks, Residual Network , Random Forest Classifier, Ensemble Model.

Video Explanation for Published paper

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