Ensemble Deep Learning for Multilingual Sign Language Translation and Recognition

Ancylin Albert P, Karumanchi Dolly Sree, Nivethitha R

2025

Abstract

This research introduces an advanced system for instantaneous sign language interpretation and conversion, employing a fusion of sophisticated neural networks such as ResNet, DenseNet, and EfficientNet. The innovative technology seeks to bridge the communication gap between hearing-impaired and hearing individuals by precisely decoding sign language movements and converting them into spoken language.The system accommodates various input formats and provides instant translations in multiple languages. Empirical tests demonstrate that the EfficientNet model achieved superior performance with a 99.8% accuracy rate, surpassing other models. This innovation enhances communication accessibility for the deaf community and enables seamless interaction across language barriers. Ongoing research will concentrate on improving computational efficiency and expanding language support capabilities.

Paper Citation

in Harvard Style

Albert P A., Dolly Sree K. and R N. (2025). Ensemble Deep Learning for Multilingual Sign Language Translation and Recognition. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 769-775. DOI: 10.5220/0013602200004664

in Bibtex Style

@conference{incoft25,
author={Ancylin Albert P and Karumanchi Dolly Sree and Nivethitha R},
title={Ensemble Deep Learning for Multilingual Sign Language Translation and Recognition},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={769-775},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013602200004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Ensemble Deep Learning for Multilingual Sign Language Translation and Recognition
SN - 978-989-758-763-4
AU - Albert P A.
AU - Dolly Sree K.
AU - R N.
PY - 2025
SP - 769
EP - 775
DO - 10.5220/0013602200004664
PB - SciTePress