Real time prediction of american sign language using convolutional neural networks

Shobhit Sinha; Siddhartha Singh; Sumanu Rawat; Aman Chopra

doi:10.1007/978-981-13-9939-8_3

Real time prediction of american sign language using convolutional neural networks

Shobhit Sinha
, Siddhartha Singh
, Sumanu Rawat
, Aman Chopra^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

The American Sign Language (ASL) was developed in the early 19^th century in the American School for Deaf, United States of America. It is a natural language inspired by the French sign language and is used by around half a million people around the world with a majority in North America. The Deaf Culture views deafness as a difference in human experience rather than a disability, and ASL plays an important role in this experience. In this project, we have used Convolutional Neural Networks to create a robust model that understands 29 ASL characters (26 alphabets and 3 special characters). We further host our model locally over a real-time video interface which provides the predictions in real-time and displays the corresponding English characters on the screen like subtitles. We look at the application as a one-way translator from ASL to English for the alphabet. We conceptualize this whole procedure in our paper and explore some useful applications that can be implemented.

Original language	English
Title of host publication	Advances in Computing and Data Sciences - 3rd International Conference, ICACDS 2019, Revised Selected Papers
Editors	Mayank Singh, P.K. Gupta, Vipin Tyagi, Jan Flusser, Tuncer Ören, Rekha Kashyap
Publisher	Springer Verlag
Pages	22-31
Number of pages	10
ISBN (Print)	9789811399381
DOIs	https://doi.org/10.1007/978-981-13-9939-8_3
Publication status	Published - 01-01-2019
Externally published	Yes
Event	3rd International Conference on Advances in Computing and Data Sciences, ICACDS 2019 - Ghazibad, India Duration: 12-04-2019 → 13-04-2019

Publication series

Name	Communications in Computer and Information Science
Volume	1045
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	3rd International Conference on Advances in Computing and Data Sciences, ICACDS 2019
Country/Territory	India
City	Ghazibad
Period	12-04-19 → 13-04-19

All Science Journal Classification (ASJC) codes

General Computer Science
General Mathematics

Access to Document

10.1007/978-981-13-9939-8_3

Cite this

Sinha, S., Singh, S., Rawat, S., & Chopra, A. (2019). Real time prediction of american sign language using convolutional neural networks. In M. Singh, P. K. Gupta, V. Tyagi, J. Flusser, T. Ören, & R. Kashyap (Eds.), Advances in Computing and Data Sciences - 3rd International Conference, ICACDS 2019, Revised Selected Papers (pp. 22-31). (Communications in Computer and Information Science; Vol. 1045). Springer Verlag. https://doi.org/10.1007/978-981-13-9939-8_3

Sinha, Shobhit ; Singh, Siddhartha ; Rawat, Sumanu et al. / Real time prediction of american sign language using convolutional neural networks. Advances in Computing and Data Sciences - 3rd International Conference, ICACDS 2019, Revised Selected Papers. editor / Mayank Singh ; P.K. Gupta ; Vipin Tyagi ; Jan Flusser ; Tuncer Ören ; Rekha Kashyap. Springer Verlag, 2019. pp. 22-31 (Communications in Computer and Information Science).

@inproceedings{387170be72184b2e808c3567fc56ed0e,

title = "Real time prediction of american sign language using convolutional neural networks",

abstract = "The American Sign Language (ASL) was developed in the early 19th century in the American School for Deaf, United States of America. It is a natural language inspired by the French sign language and is used by around half a million people around the world with a majority in North America. The Deaf Culture views deafness as a difference in human experience rather than a disability, and ASL plays an important role in this experience. In this project, we have used Convolutional Neural Networks to create a robust model that understands 29 ASL characters (26 alphabets and 3 special characters). We further host our model locally over a real-time video interface which provides the predictions in real-time and displays the corresponding English characters on the screen like subtitles. We look at the application as a one-way translator from ASL to English for the alphabet. We conceptualize this whole procedure in our paper and explore some useful applications that can be implemented.",

author = "Shobhit Sinha and Siddhartha Singh and Sumanu Rawat and Aman Chopra",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd 2019.; 3rd International Conference on Advances in Computing and Data Sciences, ICACDS 2019 ; Conference date: 12-04-2019 Through 13-04-2019",

year = "2019",

month = jan,

day = "1",

doi = "10.1007/978-981-13-9939-8\_3",

language = "English",

isbn = "9789811399381",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "22--31",

editor = "Mayank Singh and P.K. Gupta and Vipin Tyagi and Jan Flusser and Tuncer {\"O}ren and Rekha Kashyap",

booktitle = "Advances in Computing and Data Sciences - 3rd International Conference, ICACDS 2019, Revised Selected Papers",

address = "Germany",

}

Sinha, S, Singh, S, Rawat, S & Chopra, A 2019, Real time prediction of american sign language using convolutional neural networks. in M Singh, PK Gupta, V Tyagi, J Flusser, T Ören & R Kashyap (eds), Advances in Computing and Data Sciences - 3rd International Conference, ICACDS 2019, Revised Selected Papers. Communications in Computer and Information Science, vol. 1045, Springer Verlag, pp. 22-31, 3rd International Conference on Advances in Computing and Data Sciences, ICACDS 2019, Ghazibad, India, 12-04-19. https://doi.org/10.1007/978-981-13-9939-8_3

Real time prediction of american sign language using convolutional neural networks. / Sinha, Shobhit; Singh, Siddhartha; Rawat, Sumanu et al.
Advances in Computing and Data Sciences - 3rd International Conference, ICACDS 2019, Revised Selected Papers. ed. / Mayank Singh; P.K. Gupta; Vipin Tyagi; Jan Flusser; Tuncer Ören; Rekha Kashyap. Springer Verlag, 2019. p. 22-31 (Communications in Computer and Information Science; Vol. 1045).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Real time prediction of american sign language using convolutional neural networks

AU - Sinha, Shobhit

AU - Singh, Siddhartha

AU - Rawat, Sumanu

AU - Chopra, Aman

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd 2019.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - The American Sign Language (ASL) was developed in the early 19th century in the American School for Deaf, United States of America. It is a natural language inspired by the French sign language and is used by around half a million people around the world with a majority in North America. The Deaf Culture views deafness as a difference in human experience rather than a disability, and ASL plays an important role in this experience. In this project, we have used Convolutional Neural Networks to create a robust model that understands 29 ASL characters (26 alphabets and 3 special characters). We further host our model locally over a real-time video interface which provides the predictions in real-time and displays the corresponding English characters on the screen like subtitles. We look at the application as a one-way translator from ASL to English for the alphabet. We conceptualize this whole procedure in our paper and explore some useful applications that can be implemented.

AB - The American Sign Language (ASL) was developed in the early 19th century in the American School for Deaf, United States of America. It is a natural language inspired by the French sign language and is used by around half a million people around the world with a majority in North America. The Deaf Culture views deafness as a difference in human experience rather than a disability, and ASL plays an important role in this experience. In this project, we have used Convolutional Neural Networks to create a robust model that understands 29 ASL characters (26 alphabets and 3 special characters). We further host our model locally over a real-time video interface which provides the predictions in real-time and displays the corresponding English characters on the screen like subtitles. We look at the application as a one-way translator from ASL to English for the alphabet. We conceptualize this whole procedure in our paper and explore some useful applications that can be implemented.

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U2 - 10.1007/978-981-13-9939-8_3

DO - 10.1007/978-981-13-9939-8_3

M3 - Conference contribution

AN - SCOPUS:85073913983

SN - 9789811399381

T3 - Communications in Computer and Information Science

SP - 22

EP - 31

BT - Advances in Computing and Data Sciences - 3rd International Conference, ICACDS 2019, Revised Selected Papers

A2 - Singh, Mayank

A2 - Gupta, P.K.

A2 - Tyagi, Vipin

A2 - Flusser, Jan

A2 - Ören, Tuncer

A2 - Kashyap, Rekha

PB - Springer Verlag

T2 - 3rd International Conference on Advances in Computing and Data Sciences, ICACDS 2019

Y2 - 12 April 2019 through 13 April 2019

ER -

Sinha S, Singh S, Rawat S, Chopra A. Real time prediction of american sign language using convolutional neural networks. In Singh M, Gupta PK, Tyagi V, Flusser J, Ören T, Kashyap R, editors, Advances in Computing and Data Sciences - 3rd International Conference, ICACDS 2019, Revised Selected Papers. Springer Verlag. 2019. p. 22-31. (Communications in Computer and Information Science). doi: 10.1007/978-981-13-9939-8_3

Real time prediction of american sign language using convolutional neural networks

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