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20 May 2024
Reseach Article

Recognition of Indian Sign Language in Live Video

by Joyeeta Singha, Karen Das
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Number 19
Year of Publication: 2013
Authors: Joyeeta Singha, Karen Das
10.5120/12174-7306

Joyeeta Singha, Karen Das . Recognition of Indian Sign Language in Live Video. International Journal of Computer Applications. 70, 19 ( May 2013), 17-22. DOI=10.5120/12174-7306

@article{ 10.5120/12174-7306,
author = { Joyeeta Singha, Karen Das },
title = { Recognition of Indian Sign Language in Live Video },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 70 },
number = { 19 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume70/number19/12174-7306/ },
doi = { 10.5120/12174-7306 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:33:16.352607+05:30
%A Joyeeta Singha
%A Karen Das
%T Recognition of Indian Sign Language in Live Video
%J International Journal of Computer Applications
%@ 0975-8887
%V 70
%N 19
%P 17-22
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sign Language Recognition has emerged as one of the important area of research in Computer Vision. The difficulty faced by the researchers is that the instances of signs vary with both motion and appearance. Thus, in this paper a novel approach for recognizing various alphabets of Indian Sign Language is proposed where continuous video sequences of the signs have been considered. The proposed system comprises of three stages: Preprocessing stage, Feature Extraction and Classification. Preprocessing stage includes skin filtering, histogram matching. Eigen values and Eigen Vectors were considered for feature extraction stage and finally Eigen value weighted Euclidean distance is used to recognize the sign. It deals with bare hands, thus allowing the user to interact with the system in natural way. We have considered 24 different alphabets in the video sequences and attained a success rate of 96. 25%.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Indian Sign Language (ISL) Skin Filtering Eigen value Eigen vector Euclidean Distance (ED) Computer Vision