CFP last date
20 January 2025
Reseach Article

Indian Sign Language Numeral Recognition - An Image Processing Approach

by Pooja Kiranalli, S. R. Gengaje
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 7
Year of Publication: 2016
Authors: Pooja Kiranalli, S. R. Gengaje
10.5120/ijca2016910866

Pooja Kiranalli, S. R. Gengaje . Indian Sign Language Numeral Recognition - An Image Processing Approach. International Journal of Computer Applications. 146, 7 ( Jul 2016), 24-27. DOI=10.5120/ijca2016910866

@article{ 10.5120/ijca2016910866,
author = { Pooja Kiranalli, S. R. Gengaje },
title = { Indian Sign Language Numeral Recognition - An Image Processing Approach },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 7 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 24-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number7/25411-2016910866/ },
doi = { 10.5120/ijca2016910866 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:47.037890+05:30
%A Pooja Kiranalli
%A S. R. Gengaje
%T Indian Sign Language Numeral Recognition - An Image Processing Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 7
%P 24-27
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, Sign language is an important research problem for communicating with hearing impaired people without the help of interpreter. Hand gesture is one of the methods used in sign language which is most commonly used by deaf and dumb people to communicate with each other or with normal people. The proposed algorithm aims at developing real time image processing based system for hand gesture recognition on personal computer with an USB web cam. This paper proposes a method to detect and recognize the static image of Indian Sign Language numbering system from zero to nine. The method is based on counting the open fingers in the static images. The proposed algorithm for gesture recognition is based on boundary tracing and finger tip detection and also deals with images of bare hands, which allows the signer to interact with the system in a natural way. The proposed algorithm is first detect and segments the hand region from the real time captured images. Then using the proposed methodology, it locate the fingers and classifies the gesture. Further the system convert Indian signs into text and then speech using an audio file stored on PC. The algorithm is size invariant but it is orientation dependent. The proposed system is implemented using OpenCV.

References
  1. V. Bhame, R. Sreemathy, H. Dhumal, "Vision Based Calculator for Speech and Hearing Impaired using Hand Gesture Recognition,"1nternational Journal of Engineering Research & Technology (IJERl], Vol. 3, Issue 6, June - 2014.
  2. G.Fang, W.Gao, J. Ma, “Signer-independent sign language recognition based on SOFM/HMM”, Fifth IEEE International Conference on Systems, Man, and Cybernetics, vol.2,pp.132-140,4-6 April 2012.
  3. V. Bhame, R. Sreemathy, H. Dhumal, “Vision Based Hand Gesture Recognition Using Eccentric Approach for Human Computer Interaction”, 2014 International Conference on Advances in Computing. Communications and Informatics (ICACCI).
  4. V. Kulkarni and S. Lokhande, “Appearance Based Recognition of American Sign Language Using Gesture Segmentation”, International Jouranal on Computer Science and Engineering (IJCSE), ISSN: 0975-3397, vol. 02, No, 03, pp.560-565, March 2010.
  5. Joon-Kee Cho, Dong Ryeol park and Yeon-Ho Kim, “A Method of Remote Control for Home Appliance Using Free Hand Gesture”, IEEE International conference on Consumer Electronics, ISSN: 0673-3867, June 2012.
  6. Prashanth Suresh and Niraj Vasudevan, “Computer-aided interpreter for hearing and speech impaired”, Fourth International Conference on Computational Intelligence, Communication Systems and Networks, June 2012.
  7. Kook-Yeol Yoo, “Robust Hand Segmentation and Tracking to Illumination Variation”, in Proc. IEEE Internatioanl Conference on Consumer Electronics (ICCE), September 2014.
  8. Ruiduo Yang and Sudeep Sarkar,” Gesture Recognition using Hidden Markov Models From Fragmented Observations”,in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), June 2006.
  9. Daeho Lee and Youngtae Park,”Vision-Based Remote Control System by Motion Detection and Open Finger Counting”, in Proc. IEEE Transactions on Consumer Electronics, Vol.55, No.4, November 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Hand Gesture Recognition(HGR) Human Computer Interation(HCI) Region of Interest (ROI).