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Real Time Static and Dynamic Sign Language Recognition using Deep Learning

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Title Real Time Static and Dynamic Sign Language Recognition using Deep Learning
 
Creator Jayanthi, P
Bhama, Ponsy R K Sathia
Swetha, K
Subash, S A
 
Subject Deaf-mute people
Human-machine interaction
Inception deep-convolution network
Key frame extraction
Video analytics
 
Description 1186-1194
Sign language recognition systems are used for enabling communication between deaf-mute people and normal user.
Spatial localization of the hands could be a challenging task when hands-only occupies 10% of the entire image. This is
overcome by designing a real-time efficient system that is capable of performing the task of extraction, recognition, and
classification within a single network with the use of a deep convolution network. The recognition is performed for static
image dataset with a simple and complex background, dynamic video dataset. Static image dataset is trained and tested
using a 2D deep-convolution neural network whereas dynamic video dataset is trained and tested using a 3D deepconvolution
neural network. Spatial augmentation is done to increase the number of images of static dataset and key-frame
extraction to extract the key-frames from the videos for dynamic dataset. To improve the system performance and accuracy
Batch-Normalization layer is added to the convolution network. The accuracy is nearly 99% for dataset with a simple
background, 92% for dataset with complex background, and 84% for the video dataset. By obtaining a good accuracy, the
system is proved to be real-time efficient in recognizing and interpreting the sign language gestures.
 
Date 2022-11-24T05:57:14Z
2022-11-24T05:57:14Z
2022-11
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/60920
https://doi.org/10.56042/jsir.v81i11.52657
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(11) [Nov 2022]