Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)

<Previous Article In Volume
Next Article In Volume>

SignEase: Intuitive Sign Language Translation with Enhanced Features

Authors
Afroz Sadaf1, *, Vedant Bahadure2, Apurbo Mondal3, Amol Rindhe4
1G. H. Raisoni College of Engineering and Management, Pune, India
2G. H. Raisoni College of Engineering and Management, Pune, India
3G. H. Raisoni College of Engineering and Management, Pune, India
4G. H. Raisoni College of Engineering and Management, Pune, India
*Corresponding author.
Corresponding Author
Afroz Sadaf
Available Online 22 June 2025.
DOI
10.2991/978-94-6463-738-0_67How to use a DOI?
Keywords
Sign Language Recognition; Machine Learning; MediaPipe Landmark Detection; Text-to-Speech (TTS); Real-Time Translation; Accessibility Solutions; Inclusive Communication Technology
Abstract

Communication is a cornerstone of human interaction, yet individuals with hearing and speech impairments face significant barriers in daily life. Over 430 million people worldwide experience disabling hearing loss, many relying on sign language as their primary mode of communication. This creates a substantial communication gap with spoken language users. This paper introduces SignEase, a platform designed to bridge this gap using machine learning and MediaPipe’s landmark detection system for precise sign language recognition. The system incorporates a Convolutional Neural Network (CNN) model trained on hand landmarks and employs Text-to-Speech (TTS) technology to convert recognized signs into spoken language. The methodology involves extracting hand landmarks, training the model on these features, and implementing real-time translation into text and speech. Key findings highlight SignEase’s accuracy and efficiency in facilitating seamless communication between deaf and hearing individuals. By fostering inclusivity, improving accessibility, and enhancing opportunities for social and educational integration, SignEase represents a significant advancement in addressing communication challenges for the deaf and hard of hearing communities.

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.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
Series
Advances in Intelligent Systems Research
Publication Date
22 June 2025
ISBN
978-94-6463-738-0
ISSN
1951-6851
DOI
10.2991/978-94-6463-738-0_67How 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  - Afroz Sadaf
AU  - Vedant Bahadure
AU  - Apurbo Mondal
AU  - Amol Rindhe
PY  - 2025
DA  - 2025/06/22
TI  - SignEase: Intuitive Sign Language Translation with Enhanced Features
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 860
EP  - 872
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_67
DO  - 10.2991/978-94-6463-738-0_67
ID  - Sadaf2025
ER  -