Real-Time Sign Language Detection and Assistive System Using YOLOv10 for Enhanced Communication and Learning

The Sign Language Detection and Assistive System is an innovative system that solves the communication barriers of deaf and mute people. Advanced computer vision methods and artificial intelligence, particularly the YOLOv10 object detection model are utilized to design a real-time system that interprets American Sign Language (ASL) gestures into speech and text output.The system with advanced machine learning algorithms to facilitate instant translation successfully fills the communication gap between deaf - mute people and strangers to sign language, enabling smooth interaction. Apart from enabling smooth communication, the system has an educational component that helps new learners learn ASL. Through the display of known signs along with their respective text and audio translations, the device enables users to learn ASL gestures interactively and naturally, promoting the learning and use of sign language. The new solution not only improves accessibility and autonomy for deaf and mute people but also encourages social inclusion by offering a dual way for communication and learning. It enables people to communicate more effectively in personal, educational, and professional levels, leading to a more inclusive society.

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Publication Details

Published
2025-05-09
DOI
https://doi.org/10.1109/icepe65965.2025.11139585
Citations
1
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
1.94
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article

Real-Time Sign Language Detection and Assistive System Using YOLOv10 for Enhanced Communication and Learning

S Sandosh, G Dhaarini, R Sanjai
1 citations
Hand Gesture Recognition Systems
1.94
article

Real-Time Sign Language Detection and Assistive System Using YOLOv10 for Enhanced Communication and Learning

S Sandosh, G Dhaarini, R Sanjai
article en
1 citations

Abstract

The Sign Language Detection and Assistive System is an innovative system that solves the communication barriers of deaf and mute people. Advanced computer vision methods and artificial intelligence, particularly the YOLOv10 object detection model are utilized to design a real-time system that interprets American Sign Language (ASL) gestures into speech and text output.The system with advanced machine learning algorithms to facilitate instant translation successfully fills the communication gap between deaf - mute people and strangers to sign language, enabling smooth interaction. Apart from enabling smooth communication, the system has an educational component that helps new learners learn ASL. Through the display of known signs along with their respective text and audio translations, the device enables users to learn ASL gestures interactively and naturally, promoting the learning and use of sign language. The new solution not only improves accessibility and autonomy for deaf and mute people but also encourages social inclusion by offering a dual way for communication and learning. It enables people to communicate more effectively in personal, educational, and professional levels, leading to a more inclusive society.

Vellore Institute of Technology University (IN)
Openalex Percentile: Top 13%
Hand Gesture Recognition Systems
1.94
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Real-Time Sign Language Detection and Assistive System Using YOLOv10 for Enhanced Communication and Learning — S Sandosh, G Dhaarini, et al. · (2025) | TGRS Research Map | TGRS