Sign Language Recognition System


Authors : Likhitha K; Sahana H J; Niharika B R; Abhishek Raju; Prathima M G

Volume/Issue : Volume 7 - 2022, Issue 7 - July

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3MLdcWE

DOI : https://doi.org/10.5281/zenodo.7217973

Abstract : The goal of vision-based sign language recognition is to improve communication for the hearing impaired. However, the majority of the available sign language datasets are constrained. Real-time hand sign language identification is a problem in the world of computer vision due to factors including hand occlusion, rapid hand movement, and complicated backgrounds. In this study, we develop a deep learning-based architecture for effective sign language recognition using Single Shot Detector (SSD), 2D Convolutional Neural Network (2DCNN), 3D Convolutional Neural Network (3DCNN), and Long Short-Term Memory (LSTM) from Depth and RGB input films

Keywords : Sign Language Recognition System, Multi Modal Approach, Skeleton Based.

The goal of vision-based sign language recognition is to improve communication for the hearing impaired. However, the majority of the available sign language datasets are constrained. Real-time hand sign language identification is a problem in the world of computer vision due to factors including hand occlusion, rapid hand movement, and complicated backgrounds. In this study, we develop a deep learning-based architecture for effective sign language recognition using Single Shot Detector (SSD), 2D Convolutional Neural Network (2DCNN), 3D Convolutional Neural Network (3DCNN), and Long Short-Term Memory (LSTM) from Depth and RGB input films

Keywords : Sign Language Recognition System, Multi Modal Approach, Skeleton Based.

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe