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Reseach Article

ASL Number Recognition using Open-finger Distance Feature Measurement Technique

by Asha Thalange, Shantanu Dixit
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 8
Year of Publication: 2014
Authors: Asha Thalange, Shantanu Dixit
10.5120/18929-0302

Asha Thalange, Shantanu Dixit . ASL Number Recognition using Open-finger Distance Feature Measurement Technique. International Journal of Computer Applications. 108, 8 ( December 2014), 6-9. DOI=10.5120/18929-0302

@article{ 10.5120/18929-0302,
author = { Asha Thalange, Shantanu Dixit },
title = { ASL Number Recognition using Open-finger Distance Feature Measurement Technique },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 8 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 6-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number8/18929-0302/ },
doi = { 10.5120/18929-0302 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:42:25.961432+05:30
%A Asha Thalange
%A Shantanu Dixit
%T ASL Number Recognition using Open-finger Distance Feature Measurement Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 8
%P 6-9
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, lot of research is done in regard to the use of computers to recognize sign language. Computer recognition of sign language is an important research problem for enabling communication with hearing impaired people without the help of interpreter. In this article we propose a method to detect the static image based number of American Sign Language (ASL). This method is based on counting the open fingers in the static images and extracting the feature vector based on the successive distance between the adjacent open fingers. Further neural network is used for the classification of these numbers. This method is qualified to provide an average recognition rate of 92 percent.

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Index Terms

Computer Science
Information Sciences

Keywords

ASL Number Neural Network Static Hand Gesture Recognition Open-finger Distance Thinning.