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Recognizing and Interpreting Sign Language Gesture for Human Robot Interaction

by Shekhar Singh, Akshat Jain, Deepak Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 11
Year of Publication: 2012
Authors: Shekhar Singh, Akshat Jain, Deepak Kumar
10.5120/8247-1758

Shekhar Singh, Akshat Jain, Deepak Kumar . Recognizing and Interpreting Sign Language Gesture for Human Robot Interaction. International Journal of Computer Applications. 52, 11 ( August 2012), 24-31. DOI=10.5120/8247-1758

@article{ 10.5120/8247-1758,
author = { Shekhar Singh, Akshat Jain, Deepak Kumar },
title = { Recognizing and Interpreting Sign Language Gesture for Human Robot Interaction },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 11 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 24-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number11/8247-1758/ },
doi = { 10.5120/8247-1758 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:51:59.964327+05:30
%A Shekhar Singh
%A Akshat Jain
%A Deepak Kumar
%T Recognizing and Interpreting Sign Language Gesture for Human Robot Interaction
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 11
%P 24-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Visual interpretation of sign language gesture can be useful in accomplishing natural human robot interaction. This paper describes a sign language gesture based recognition, interpreting and imitation learning system using Indian Sign Language for performing Human Robot Interaction in real time. It permits us to construct a convenient sign language gesture based communication with humanoid robot. The classification, recognition, learning, interpretation process is carried out by extracting the features from Indian sign language (ISL) gestures. Chain code and fisher score is considered as a feature vector for classification and recognition process. It is to be done by the two statistical approaches namely known as Hidden Markov Model (HMM) technique and feed forward back propagation neural network (FNN) in order to achieve satisfactory recognition accuracy. The sensitivity, specificity and accuracy were found to be equal 98. 60%, 97. 64% and 97. 52% respectively. It can be concluded that FNN gives fast and accurate recognition and it works as promising tool for recognition and interpretation of sign language gesture for human computer interaction. The overall accuracy of recognition and interpretation of the proposed system is 95. 34%. Thus, this approach is suitable for automated real time human computer interaction tool.

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Index Terms

Computer Science
Information Sciences

Keywords

HRI ISL CLAHE Chain Code HMM Fisher Score FNN Gesture recognizing Gesture Interpretation

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