Using convolutional neural networks for fingerspelling sign recognition in brazilian sign language-Reference-Cited by-同舟云学术

Using convolutional neural networks for fingerspelling sign recognition in brazilian sign language

Published:2019-10-29 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 25th Brazillian Symposium on Multimedia and the Web
language:
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Author:

Lima Douglas F. L.¹,Neto Armando S. Salvador¹,Santos Ewerton N.¹,Araujo Tiago Maritan U.¹,Rêgo Thais Gaudencio do²

Affiliation:

1. LAVID/CI/UFPB, João Pessoa, Brazil

2. VISIO/CI/UFPB, João Pessoa, Brazil

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3323503.3360306

Reference27 articles.

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2. P. Lokhandle R. Prajapati S. Pansare. Data Gloves for Sign Language Recognition System. International Journal of Computer Applications (0975 - 8887). P. Lokhandle R. Prajapati S. Pansare. Data Gloves for Sign Language Recognition System. International Journal of Computer Applications (0975 - 8887) .

3. A cost effective Sign Language to voice emulation system; Proceedings of the 2015 Eighth International Conference on Contemporary Computing (IC3).; Noida;Bhatnagar V.S.;India. 20--,2015

4. World Health Organization. Deafness prevention (2018). Available in: <https://www.who.int/deafness/estimates/en/>. World Health Organization. Deafness prevention (2018). Available in: <https://www.who.int/deafness/estimates/en/>.

5. A.G. Howard M. Zhu B. Chen D. Kelenichenko W. Wang T. Weyand M. Andreetto and H. Adam. Efficient Convolutional Neural Networks for Mobile Vision. arXiv:1704.04861v1 [cs.CV] 17 Apr 2017. A.G. Howard M. Zhu B. Chen D. Kelenichenko W. Wang T. Weyand M. Andreetto and H. Adam. Efficient Convolutional Neural Networks for Mobile Vision. arXiv:1704.04861v1 [cs.CV] 17 Apr 2017.

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