Indian Sign Language Translator Using CNN

Authors

  • Aadhya Satrasala School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India
  • Anish B K Koundinya School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India
  • Devadula Gayatri School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India
  • Seshadri Lasya School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India
  • Anil Kumar Ambore School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India

DOI:

https://doi.org/10.5281/zenodo.15279424

Keywords:

Indian Sign Language Translator, MediaPipe, Convolutional Neural Network (CNN), Feed Forward Neural Networks, Hand Gesture Recognition

Abstract

This paper main focus is to create a real-time Indian Sign Language (ISL) translator designed to overcome the gap between the deaf and hard-of-hearing population and the hearing population. By leveraging computer vision techniques and machine learning models, the system can accurately recognize a wide range of ISL gestures and translate them into corresponding text outputs in English.  The application is intended to facilitate seamless communication, enhancing accessibility in various settings such as education, healthcare, and daily interactions. This solution aims to foster greater inclusion and social integration for ISL users while addressing the lack of real-time ISL translation tools in India.

References

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Published

2025-04-25

How to Cite

Aadhya Satrasala, Anish B K Koundinya, Devadula Gayatri, Seshadri Lasya, & Anil Kumar Ambore. (2025). Indian Sign Language Translator Using CNN. International Journal of Computational Learning and Intelligence, An Open AI Journal, 4(4), 792–798. https://doi.org/10.5281/zenodo.15279424

Issue

Vol. 4 No. 4 (2025): October

Section

RESEARCH ARTICLES

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Copyright (c) 2025 Aadhya Satrasala, Anish B K Koundinya, Devadula Gayatri, Seshadri Lasya, Anil Kumar Ambore

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