Hand Speak: An AI-Powered Real-Time System for Sign Language Recognition and Seamless Translation

DOI

10.2991/978-94-6463-858-5_247How to use a DOI?

Keywords

Real-Time Gesture Recognition; Sign Language Translation; Convolutional Neural Networks (CNN); Deep Learning; Hand Gesture Classification; AI-Powered Accessibility; Sign-to-Text Conversion; Text-to-Speech Integration; Assistive Technology; Human-Computer Interaction; Transfer Learning; Computer Vision; Natural Language Processing (NLP)

Abstract

Sign language is a fundamental mode of communication for the deaf and hard-of-hearing communities, yet real-time automated translation remains a significant challenge due to the complexity of hand gestures, variations in lighting, and differences in signing styles. This study introduces Hand Speak, an advanced AI-driven real-time sign language recognition and translation system that leverages deep learning techniques to bridge the communication gap between signers and non-signers. The system employs a custom-optimized Convolutional Neural Network (CNN) to accurately recognize and interpret sign language gestures from live video input. By integrating computer vision techniques, spatiotemporal analysis, and optimized feature extraction, the proposed model ensures high recognition accuracy while maintaining minimal latency, making it suitable for real-world applications. Unlike traditional machine learning approaches, which often suffer from limited scalability and poor generalization to different sign languages, the proposed system efficiently adapts to dynamic environments by employing transfer learning and real-time gesture tracking. Additionally, Natural Language Processing (NLP) is integrated to enhance sign-to-text conversion, facilitating a seamless transition between visual gestures and spoken or written language. The system also includes text-to-speech (TTS) integration, allowing recognized gestures to be converted into synthesized speech for enhanced accessibility. Comprehensive experimental evaluations on benchmark sign language datasets and real-world testing scenarios demonstrate that the proposed model significantly outperforms conventional recognition techniques in terms of speed, robustness, and accuracy. The study also explores potential applications, including educational tools, accessibility solutions, and AI-powered assistive devices. This research marks a significant step toward the development of intelligent, real-time sign language translation systems, paving the way for inclusive communication technologies that empower the deaf and hard-of-hearing communities worldwide.

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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TY  - CONF
AU  - Tanaya Kanungo
AU  - S. Aswini
AU  - S.  Neha
AU  - Valarmathi Ramasamy
PY  - 2025
DA  - 2025/11/04
TI  - Hand Speak: An AI-Powered Real-Time System for Sign Language Recognition and Seamless Translation
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2946
EP  - 2958
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_247
DO  - 10.2991/978-94-6463-858-5_247
ID  - Kanungo2025
ER  -