A new benchmark on american sign language recognition using convolutional neural network

Md Moklesur Rahman, Md Shafiqul Islam, Md Hafizur Rahman, Roberto Sassi, Massimo W. Rivolta, Md Aktaruzzaman

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

Abstract

The listening or hearing impaired (deaf/dumb) people use a set of signs, called sign language instead of speech for communication among them. However, it is very challenging for non-sign language speakers to communicate with this community using signs. It is very necessary to develop an application to recognize gestures or actions of sign languages to make easy communication between the normal and the deaf community. The American Sign Language (ASL) is one of the mostly used sign languages in the World, and considering its importance, there are already existing methods for recognition of ASL with limited accuracy. The objective of this study is to propose a novel model to enhance the accuracy of the existing methods for ASL recognition. The study has been performed on the alphabet and numerals of four publicly available ASL datasets. After preprocessing, the images of the alphabet and numerals were fed to a newly proposed convolutional neural network (CNN) model, and the performance of this model was evaluated to recognize the numerals and alphabet of these datasets. The proposed CNN model significantly (9%) improves the recognition accuracy of ASL reported by some existing prominent methods.

Original languageEnglish (US)
Title of host publication2019 International Conference on Sustainable Technologies for Industry 4.0, STI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728160979
DOIs
StatePublished - Dec 2019
Event2019 International Conference on Sustainable Technologies for Industry 4.0, STI 2019 - Dhaka, Bangladesh
Duration: Dec 24 2019Dec 25 2019

Publication series

Name2019 International Conference on Sustainable Technologies for Industry 4.0, STI 2019

Conference

Conference2019 International Conference on Sustainable Technologies for Industry 4.0, STI 2019
CountryBangladesh
CityDhaka
Period12/24/1912/25/19

Keywords

  • American Sign Language
  • ASL
  • Convolution neural network
  • Hand gesture
  • Recognition

ASJC Scopus subject areas

  • Information Systems and Management
  • Renewable Energy, Sustainability and the Environment
  • Health Informatics
  • Education
  • Agronomy and Crop Science
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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  • Cite this

    Rahman, M. M., Islam, M. S., Rahman, M. H., Sassi, R., Rivolta, M. W., & Aktaruzzaman, M. (2019). A new benchmark on american sign language recognition using convolutional neural network. In 2019 International Conference on Sustainable Technologies for Industry 4.0, STI 2019 [9067974] (2019 International Conference on Sustainable Technologies for Industry 4.0, STI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/STI47673.2019.9067974