Real-time sign language recognition with guided deep convolutional neural networks

Zhengzhe Liu; Fuyang Huang; Gladys Wai Lan Tang; Felix Yim Binh Sze; Jing Qin; Xiaogang Wang; Qiang Xu

doi:10.1145/2983310.2989187

Real-time sign language recognition with guided deep convolutional neural networks

Zhengzhe Liu, Fuyang Huang, Gladys Wai Lan Tang, Felix Yim Binh Sze, Jing Qin, Xiaogang Wang, Qiang Xu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

10 Citations (Scopus)

Abstract

We develop a real-time, robust and accurate sign language recognition system leveraging deep convolutional neural networks(DCNN). Our framework is able to prevent common problems such as error accumulation of existing frameworks and it outperforms state-of-the-art frameworks in terms of accuracy, recognition time and usability.

Original language	English
Title of host publication	SUI 2016 - Proceedings of the 2016 Symposium on Spatial User Interaction
Publisher	Association for Computing Machinery, Inc
Pages	187
Number of pages	1
ISBN (Electronic)	9781450340687
DOIs	https://doi.org/10.1145/2983310.2989187
Publication status	Published - 15 Oct 2016
Externally published	Yes
Event	4th Symposium on Spatial User Interaction, SUI 2016 - Tokyo, Japan Duration: 15 Oct 2016 → 16 Oct 2016

Conference

Conference	4th Symposium on Spatial User Interaction, SUI 2016
Country/Territory	Japan
City	Tokyo
Period	15/10/16 → 16/10/16

Keywords

Convolutional neural networks
Sign language recognition

ASJC Scopus subject areas

Human-Computer Interaction

More information

10.1145/2983310.2989187

Cite this

@inproceedings{53c4a0d756f34438ba04805f7f2fbff6,

title = "Real-time sign language recognition with guided deep convolutional neural networks",

abstract = "We develop a real-time, robust and accurate sign language recognition system leveraging deep convolutional neural networks(DCNN). Our framework is able to prevent common problems such as error accumulation of existing frameworks and it outperforms state-of-the-art frameworks in terms of accuracy, recognition time and usability.",

keywords = "Convolutional neural networks, Sign language recognition",

author = "Zhengzhe Liu and Fuyang Huang and Tang, {Gladys Wai Lan} and Sze, {Felix Yim Binh} and Jing Qin and Xiaogang Wang and Qiang Xu",

year = "2016",

month = oct,

day = "15",

doi = "10.1145/2983310.2989187",

language = "English",

pages = "187",

booktitle = "SUI 2016 - Proceedings of the 2016 Symposium on Spatial User Interaction",

publisher = "Association for Computing Machinery, Inc",

note = "4th Symposium on Spatial User Interaction, SUI 2016 ; Conference date: 15-10-2016 Through 16-10-2016",

}

Liu, Z, Huang, F, Tang, GWL, Sze, FYB, Qin, J, Wang, X & Xu, Q 2016, Real-time sign language recognition with guided deep convolutional neural networks. in SUI 2016 - Proceedings of the 2016 Symposium on Spatial User Interaction. Association for Computing Machinery, Inc, pp. 187, 4th Symposium on Spatial User Interaction, SUI 2016, Tokyo, Japan, 15/10/16. https://doi.org/10.1145/2983310.2989187

Real-time sign language recognition with guided deep convolutional neural networks. / Liu, Zhengzhe; Huang, Fuyang; Tang, Gladys Wai Lan et al.
SUI 2016 - Proceedings of the 2016 Symposium on Spatial User Interaction. Association for Computing Machinery, Inc, 2016. p. 187.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Real-time sign language recognition with guided deep convolutional neural networks

AU - Liu, Zhengzhe

AU - Huang, Fuyang

AU - Tang, Gladys Wai Lan

AU - Sze, Felix Yim Binh

AU - Qin, Jing

AU - Wang, Xiaogang

AU - Xu, Qiang

PY - 2016/10/15

Y1 - 2016/10/15

N2 - We develop a real-time, robust and accurate sign language recognition system leveraging deep convolutional neural networks(DCNN). Our framework is able to prevent common problems such as error accumulation of existing frameworks and it outperforms state-of-the-art frameworks in terms of accuracy, recognition time and usability.

AB - We develop a real-time, robust and accurate sign language recognition system leveraging deep convolutional neural networks(DCNN). Our framework is able to prevent common problems such as error accumulation of existing frameworks and it outperforms state-of-the-art frameworks in terms of accuracy, recognition time and usability.

KW - Convolutional neural networks

KW - Sign language recognition

UR - http://www.scopus.com/inward/record.url?scp=84995677872&partnerID=8YFLogxK

U2 - 10.1145/2983310.2989187

DO - 10.1145/2983310.2989187

M3 - Conference article published in proceeding or book

SP - 187

BT - SUI 2016 - Proceedings of the 2016 Symposium on Spatial User Interaction

PB - Association for Computing Machinery, Inc

T2 - 4th Symposium on Spatial User Interaction, SUI 2016

Y2 - 15 October 2016 through 16 October 2016

ER -

Real-time sign language recognition with guided deep convolutional neural networks

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

Other files and links

Fingerprint

Cite this