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AI-Based Sign Language Translator
Subject area: Science,Engineering and Technology · Area of research: Computer Vision and Machine Learning
Abstract
Sign language is the primary communication method for deaf and hard-of-hearing (DHH) individuals, yet it remains unfamiliar to most non-DHH people, creating a significant communication gap. To address this, we propose an AI-powered Sign Language Translation system that utilizes computer vision and deep learning to interpret hand gestures in real time and convert them into readable text. Our system is based on a Convolutional Neural Network (CNN) model trained on American Sign Language (ASL) datasets and uses webcam input for gesture recognition. This paper outlines the design, methodology, and implementation of the system, discusses the challenges in sign language translation (SLT), and highlights possible improvements using transformer-based approaches.
Keywords
Sign Language Translation, Artificial Intelligence, Deep Learning, CNN, Computer Vision.
References
[1] G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955.
[2] D. P. Kingma and M. Welling, “Auto-encoding variational Bayes,” arXiv:1312.6114, 2013.
[3] Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987.
[4] TensorFlow Documentation, https://www.tensorflow.org/.
[5] OpenCV Documentation, https://opencv.org/.
How to cite this paper
@article{1712443,
author = {Yogesh Balaji Reddy, Prathamesh Nanasaheb Raut, Aniket Sndipan Tandale, Prajesh Vikas Surwase, Prof. D. J. Waghmare},
title = {AI-Based Sign Language Translator},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {2243-2249},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712443.pdf},
abstract = {Sign language is the primary communication method for deaf and hard-of-hearing (DHH) individuals, yet it remains unfamiliar to most non-DHH people, creating a significant communication gap. To address this, we propose an AI-powered Sign Language Translation system that utilizes computer vision and deep learning to interpret hand gestures in real time and convert them into readable text. Our system is based on a Convolutional Neural Network (CNN) model trained on American Sign Language (ASL) datasets and uses webcam input for gesture recognition. This paper outlines the design, methodology, and implementation of the system, discusses the challenges in sign language translation (SLT), and highlights possible improvements using transformer-based approaches.},
keywords = {Sign Language Translation, Artificial Intelligence, Deep Learning, CNN, Computer Vision.},
month = {November},
doi = {https://doi.org/10.64388/IREV9I5-1712443}
}