abstract

Real-time Sign Language Recognition with Guided Deep Convolutional Neural Networks

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Qiang XuAuthors Info & Claims

SUI '16: Proceedings of the 2016 Symposium on Spatial User Interaction

Page 187

https://doi.org/10.1145/2983310.2989187

Published: 15 October 2016 Publication History

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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.

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Reference

[1]

H. Wang, X. Chai, X. Hong, G. Zhao, and X. Chen. Isolated sign language recognition with grassmann covariance matrices. ACM Transactions on Accessible Computing (TACCESS), 8(4):14, 2016.

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Liu ZPang LQi X(2024)MEN: Mutual Enhancement Networks for Sign Language Recognition and EducationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.317403135:1(311-325)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3174031
Yadav YKiran VKarthik VThadikamalla GKumaran P(2024)Real Time Sign Language Recognition Using Custom Convolutional Neural Network and YOLOv5Intelligent Computing, Smart Communication and Network Technologies10.1007/978-3-031-75957-4_14(157-171)Online publication date: 20-Nov-2024
https://doi.org/10.1007/978-3-031-75957-4_14
Vahdani EJing LHuenerfauth MTian Y(2023)Multi-Modal Multi-Channel American Sign Language RecognitionInternational Journal of Artificial Intelligence and Robotics Research10.1142/S297233532450001701:01Online publication date: 20-Dec-2023
https://doi.org/10.1142/S2972335324500017
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Index Terms

Real-time Sign Language Recognition with Guided Deep Convolutional Neural Networks
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

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Information

Published In

SUI '16: Proceedings of the 2016 Symposium on Spatial User Interaction

October 2016

236 pages

ISBN:9781450340687

DOI:10.1145/2983310

General Chair:
Christian Sandor
Nara Institute of Science and Technology, Japan
,
Program Chairs:
Robert Teather
Carleton University, Canada
,
Evan Suma
University of Southern California, USA
,
Kyle Johnsen
University Of Georgia, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2016

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Funding Sources

Knowledge Transfer Project Fund (KPF)

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SUI '16

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SIGGRAPH
SIGCHI
VRSJ SIG-MR

SUI '16: Symposium on Spatial User Interaction

October 15 - 16, 2016

Tokyo, Japan

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SUI '16 Paper Acceptance Rate 20 of 77 submissions, 26%;

Overall Acceptance Rate 86 of 279 submissions, 31%

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Liu ZPang LQi X(2024)MEN: Mutual Enhancement Networks for Sign Language Recognition and EducationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.317403135:1(311-325)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3174031
Yadav YKiran VKarthik VThadikamalla GKumaran P(2024)Real Time Sign Language Recognition Using Custom Convolutional Neural Network and YOLOv5Intelligent Computing, Smart Communication and Network Technologies10.1007/978-3-031-75957-4_14(157-171)Online publication date: 20-Nov-2024
https://doi.org/10.1007/978-3-031-75957-4_14
Vahdani EJing LHuenerfauth MTian Y(2023)Multi-Modal Multi-Channel American Sign Language RecognitionInternational Journal of Artificial Intelligence and Robotics Research10.1142/S297233532450001701:01Online publication date: 20-Dec-2023
https://doi.org/10.1142/S2972335324500017
Krishnamurthi SM I(2021)Sign Language Translator Using Deep Learning Techniques2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)10.1109/ICECCT52121.2021.9616795(1-5)Online publication date: 15-Sep-2021
https://doi.org/10.1109/ICECCT52121.2021.9616795
Wang HChang LYu LDong X(2021)Optimization and Embedded Implementation of Gesture Recognition Algorithm Based on Convolutional Neural Network2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems10.1007/978-981-16-1726-3_210(1587-1592)Online publication date: 2-Jun-2021
https://doi.org/10.1007/978-981-16-1726-3_210
Liu ZQi XPang LBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)Self-boosted Gesture Interactive System with ST-NetProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240530(145-153)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3240508.3240530
Rao GSyamala KKishore PSastry A(2018)Deep convolutional neural networks for sign language recognition2018 Conference on Signal Processing And Communication Engineering Systems (SPACES)10.1109/SPACES.2018.8316344(194-197)Online publication date: Jan-2018
https://doi.org/10.1109/SPACES.2018.8316344
Ye YTian YHuenerfauth MLiu J(2018)Recognizing American Sign Language Gestures from Within Continuous Videos2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW.2018.00280(2145-214509)Online publication date: Jun-2018
https://doi.org/10.1109/CVPRW.2018.00280
Rodríguez JMartínez F(2018)Towards On-Line Sign Language Recognition Using Cumulative SD-VLAD DescriptorsAdvances in Computing10.1007/978-3-319-98998-3_29(371-385)Online publication date: 19-Aug-2018
https://doi.org/10.1007/978-3-319-98998-3_29
Tian YVahdani EJing LHuenerfauth M(undefined)Multi-Modal Multi-Channel American Sign Language RecognitionSSRN Electronic Journal10.2139/ssrn.4182158
https://doi.org/10.2139/ssrn.4182158

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