Authors : S Asrita Sreekari; Bathi Venkata Varaha Durga Yamini; Somayajula Venkata Thanmayi Sri; Maram Naga Sireesha

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/395yk2ca

Scribd : https://tinyurl.com/muaadrvn

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY1338

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In this project, a Deep Learning Convolutional Neural Network (DL-CNN) model trained on ImageNet and based on VGG16 is used to develop a Sign Language Recognition System incorporated into a mobile application. The technology recognizes a variety of hand gestures and movements that are inherent in sign language, allowing for real-time interpretation of sign language gestures that are recorded by the device's camera. Users can simply interact with the system by capturing motions in sign language and obtaining corresponding written or aural outputs for better communication through the app interface. Through improving accessibility and inclusivity for people with hearing loss, this project seeks to close gaps and promote understanding through technology by facilitating seamless communication in a variety of settings.

Keywords : VGG16, ImageNet, Convolution Neural Networks, Mobile Application.

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