Real time prediction of american sign language using convolutional neural networks

Shobhit Sinha, Siddhartha Singh, Sumanu Rawat, Aman Chopra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationAdvances in Computing and Data Sciences - 3rd International Conference, ICACDS 2019, Revised Selected Papers
EditorsMayank Singh, P.K. Gupta, Vipin Tyagi, Jan Flusser, Tuncer Ören, Rekha Kashyap
PublisherSpringer Verlag
Pages22-31
Number of pages10
ISBN (Print)9789811399381
DOIs
Publication statusPublished - 01-01-2019
Externally publishedYes
Event3rd International Conference on Advances in Computing and Data Sciences, ICACDS 2019 - Ghazibad, India
Duration: 12-04-201913-04-2019

Publication series

NameCommunications in Computer and Information Science
Volume1045
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference3rd International Conference on Advances in Computing and Data Sciences, ICACDS 2019
CountryIndia
CityGhazibad
Period12-04-1913-04-19

Fingerprint

Sign Language
Neural Networks
Neural networks
Prediction
Real-time
Disability
Natural Language
Model
Character

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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 ; Chopra, Aman. / 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).
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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.",
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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; Chopra, Aman.

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 proceedingConference contribution

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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). https://doi.org/10.1007/978-981-13-9939-8_3