Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3414
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGupta, Devashish-
dc.contributor.authorMohanty, Jaganath Prasad-
dc.contributor.authorSwain, Ayas Kant-
dc.contributor.authorMahapatra, Kamalakanta-
dc.date.accessioned2019-12-27T05:48:53Z-
dc.date.available2019-12-27T05:48:53Z-
dc.date.issued2019-12-
dc.identifier.citation5th IEEE International Symposium on Smart Electronic Systems ( IEEE-iSES 2019 ) Rourkela, India, 16- 18 December 2019.en_US
dc.identifier.urihttp://hdl.handle.net/2080/3414-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractHand 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.subjectDeep learningen_US
dc.subjectHand gesture recognitionen_US
dc.subjectSign language interpreteren_US
dc.subjectASLen_US
dc.subjectHuman-machine interfaceen_US
dc.subjectConvolutional neural networken_US
dc.subjectRaspberry pien_US
dc.titleAutoGstr: Relatively Accurate Sign Language Interpreteren_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2019_ISES_JPMohanty_AutoGstr.pdfConference paper563.71 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.