A Deep Learning Approach to Sign Language Recognition for Speech-Impaired Individuals

This research shows that many people with speech impairments struggle to find employment due to concerns about their ability to communicate with colleagues. To address this challenge, a web application has been created that integrates computer vision and deep learning techniques. The AI-driven application is trained to recognize various signs and translate them into text, facilitating understanding among all users. This work will evaluate different models and select the one with the best performance based on accuracy and minimized loss.

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Publication Details

Published
2024-11-06
DOI
https://doi.org/10.1109/icstsdg61998.2024.11026550
Primary Topic
Hand Gesture Recognition Systems
Type
article
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article

A Deep Learning Approach to Sign Language Recognition for Speech-Impaired Individuals

K. Dinakaran, Turaga Deepak
Hand Gesture Recognition Systems
article

A Deep Learning Approach to Sign Language Recognition for Speech-Impaired Individuals

K. Dinakaran, Turaga Deepak
article en

Abstract

This research shows that many people with speech impairments struggle to find employment due to concerns about their ability to communicate with colleagues. To address this challenge, a web application has been created that integrates computer vision and deep learning techniques. The AI-driven application is trained to recognize various signs and translate them into text, facilitating understanding among all users. This work will evaluate different models and select the one with the best performance based on accuracy and minimized loss.

ATA Engineering (United States) (US)
Reduced inequalities
Openalex Percentile: Top 70%
Hand Gesture Recognition Systems
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A Deep Learning Approach to Sign Language Recognition for Speech-Impaired Individuals — K. Dinakaran, Turaga Deepak · (2024) | TGRS Research Map | TGRS