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<p>Real Time Static and Dynamic Sign Language Recognition using Deep Learning</p>

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Title Statement <p>Real Time Static and Dynamic Sign Language Recognition using Deep Learning</p>
 
Added Entry - Uncontrolled Name Jayanthi, P ; Department of Computer Technology, MIT, Anna University, Chennai 600 044, Tamil Nadu, India
Bhama, Ponsy RK Sathia; Department of Computer Technology, MIT, Anna University, Chennai 600 044, Tamil Nadu, India
Swetha, K ; Department of Information Technology, MIT, Anna University, Chennai 600 044, Tamil Nadu, India
Subash, S A; Department of Information Technology, MIT, Anna University, Chennai 600 044, Tamil Nadu, India
 
Uncontrolled Index Term Deaf-mute people, Human-machine interaction, Inception deep-convolution network, Key frame extraction, Video analytics
 
Summary, etc. <p>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 deep-convolution 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.</p>
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2022-12-12 07:02:36
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/52657
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 81, ##issue.no## 11 (2022): Journal of Scientific & Industrial Research
 
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Nonspecific Relationship Entry http://op.niscair.res.in/index.php/JSIR/article/download/52657/465570809