Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/3414| Title: | AutoGstr: Relatively Accurate Sign Language Interpreter |
| Authors: | Gupta, Devashish Mohanty, Jaganath Prasad Swain, Ayas Kant Mahapatra, Kamalakanta |
| Keywords: | Deep learning Hand gesture recognition Sign language interpreter ASL Human-machine interface Convolutional neural network Raspberry pi |
| Issue Date: | Dec-2019 |
| Citation: | 5th IEEE International Symposium on Smart Electronic Systems ( IEEE-iSES 2019 ) Rourkela, India, 16- 18 December 2019. |
| 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. |
| Description: | Copyright of this document belongs to proceedings publisher. |
| URI: | http://hdl.handle.net/2080/3414 |
| Appears in Collections: | Conference Papers |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2019_ISES_JPMohanty_AutoGstr.pdf | Conference paper | 563.71 kB | Adobe PDF | View/Open |
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