Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)

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Sign Language Interpretation and Sentence Building: A CNN-Based Solution

Authors
Ananya A. Poojary1, *, Akshata Ravindra Shet1, S. R. Nisarga1
1Department of Artificial Intelligence and Data Science, Shri Madhwa Vadiraja Institute of Technology and Management, Udupi, India
*Corresponding author. Email: ananyaarunpoojary@gmail.com
Corresponding Author
Ananya A. Poojary
Available Online 16 June 2025.
DOI
10.2991/978-94-6463-762-5_16How to use a DOI?
Keywords
Sign Language Recognition; Image Classification; Convolutional Neural Network
Abstract

This project introduces an innovative real-time vision-based system designed to recognize finger spelling and interpret sign language for readabil- ity in text. Leveraging advanced computer vision and NLP techniques, the meth- odology employs a two-layer prediction algorithm built on Convolutional Neural Network (CNN) framework. By incorporating machine learning algorithms, the system achieves an impressive 98.0% accuracy in translating sign language.

Targeted at enhancing communication for deaf and hard-of-hearing individuals, the system also includes a word suggestion feature based on identified letters. Its design holds great promise for educational applications and can be extended to support various native sign languages through appropriate datasets. This approach effectively addresses communication barriers, showcasing the potential of intelligent computational solutions to improve accessibility and pro- mote understanding of sign language.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)
Series
Advances in Engineering Research
Publication Date
16 June 2025
ISBN
978-94-6463-762-5
ISSN
2352-5401
DOI
10.2991/978-94-6463-762-5_16How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ananya A. Poojary
AU  - Akshata Ravindra Shet
AU  - S. R. Nisarga
PY  - 2025
DA  - 2025/06/16
TI  - Sign Language Interpretation and Sentence Building: A CNN-Based Solution
BT  - Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)
PB  - Atlantis Press
SP  - 168
EP  - 176
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-762-5_16
DO  - 10.2991/978-94-6463-762-5_16
ID  - Poojary2025
ER  -