AutoGstr: Relatively Accurate Sign Language Interpreter

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3414

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DC Field	Value	Language
dc.contributor.author	Gupta, Devashish	-
dc.contributor.author	Mohanty, Jaganath Prasad	-
dc.contributor.author	Swain, Ayas Kant	-
dc.contributor.author	Mahapatra, Kamalakanta	-
dc.date.accessioned	2019-12-27T05:48:53Z	-
dc.date.available	2019-12-27T05:48:53Z	-
dc.date.issued	2019-12	-
dc.identifier.citation	5th IEEE International Symposium on Smart Electronic Systems ( IEEE-iSES 2019 ) Rourkela, India, 16- 18 December 2019.	en_US
dc.identifier.uri	http://hdl.handle.net/2080/3414	-
dc.description	Copyright of this document belongs to proceedings publisher.	en_US
dc.description.abstract	Hand gesture recognition is the one of the method to identify the hand position, its pattern and then translate it to the corresponding meaning or purpose. This paper contributes a real time sign language interpretation of hand gestures based on deep convolutional neural networks with focus on development of a cost-effective and efficient hardware prototype for communication ease with deaf and dumb people.	en_US
dc.subject	Deep learning	en_US
dc.subject	Hand gesture recognition	en_US
dc.subject	Sign language interpreter	en_US
dc.subject	ASL	en_US
dc.subject	Human-machine interface	en_US
dc.subject	Convolutional neural network	en_US
dc.subject	Raspberry pi	en_US
dc.title	AutoGstr: Relatively Accurate Sign Language Interpreter	en_US
dc.type	Article	en_US
Appears in Collections:	Conference Papers

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