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Reseach Article

Multiple Features based Recognition of Static American Sign Language Alphabets

Published on September 2015 by Asha Thalange, Shantanu Dixit
International Conference on Emergent Trends in Computing and Communication
Foundation of Computer Science USA
ETCC2015 - Number 2
September 2015
Authors: Asha Thalange, Shantanu Dixit
072699d7-5151-4d8f-99c0-e9f5aadeed63

Asha Thalange, Shantanu Dixit . Multiple Features based Recognition of Static American Sign Language Alphabets. International Conference on Emergent Trends in Computing and Communication. ETCC2015, 2 (September 2015), 11-16.

@article{
author = { Asha Thalange, Shantanu Dixit },
title = { Multiple Features based Recognition of Static American Sign Language Alphabets },
journal = { International Conference on Emergent Trends in Computing and Communication },
issue_date = { September 2015 },
volume = { ETCC2015 },
number = { 2 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 11-16 },
numpages = 6,
url = { /proceedings/etcc2015/number2/22337-4564/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Emergent Trends in Computing and Communication
%A Asha Thalange
%A Shantanu Dixit
%T Multiple Features based Recognition of Static American Sign Language Alphabets
%J International Conference on Emergent Trends in Computing and Communication
%@ 0975-8887
%V ETCC2015
%N 2
%P 11-16
%D 2015
%I International Journal of Computer Applications
Abstract

Communication with the hearing impaired people without the help of interpreter is a big challenge for common people. Thus efficient computer based recognition of sign language is an important research problem. Till now numbers of techniques are being developed. This article explains a novel method to recognize the 24 static image based alphabets A to Z (excluding dynamic alphabets J and Z) of American Sign Language (ASL) using two different features. This method extracts the feature vector of the images based on the simple method of orientation histogram along with the statistical parameters. Further neural network is used for the classification of these alphabets. This method is qualified to provide an average recognition rate of 93. 36 percent.

References
  1. Henrik Birk and Thomas Baltzer Moeslund, "Recognizing Gestures From the Hand Alphabet Using Principal Component Analysis", Master's Thesis, Laboratory of Image Analysis, Aalborg University, Denmark, 1996.
  2. Myron W. Krueger, Artificial Reality II, Addison-Wesley, Reading, 1991
  3. Thomas G. Zimmerman and Jaron Lanier, "A Hand Gesture Interface Device", ACM SIGCHI/GI, pages 189-192, 1987
  4. James Davis, and Mubarak Shah, "Recognizing hand gestures", ECCV, pages 331-340, Stockholm, Sweden, May 1994
  5. Arpita Ray Sarkar, G. Sanyal, S. Majumder, " Hand Gesture Recognition Systems: A Survey", International Journal of Computer Applications (0975 – 8887) Volume 71– No. 15, May 2013.
  6. Klimis Symeonidis, "Hand Gesture Recognition Using Neural Networks", Master's Thesis, School of Electronic and Electrical Engineering On August 23, 2000
  7. M. Lamar and M. Bhuiyant. "Hand alphabet recognition using morphological PCA and neural networks". International Joint Conference on Neural Networks, pages 2839–2844, Washington, USA, 1999.
  8. Jonathan C. Rupe, "Vision-Based Hand Shape Identification for Sign Language Recognition", Master's thesis, Department of Computer Engineering Kate Gleason College of Engineering Rochester Institute of Technology Rochester, NY April 2005
  9. X. Teng, B. Wu, W. Yu, and C. Liu, "A hand gesture recognition system based on local linear embedding" , Journal of Visual Languages and Computing 16 (2005) 442–454.
  10. E. Stergiopoulou, N. Papamarkos, "Hand gesture recognition using a neural network shape fitting technique", Engineering Applications of Artificial Intelligence Journal (2009)
  11. U. Rokade, D. Doye, and M. Kokare, "Hand Gesture Recognition Using Object Based Key Frame Selection", International Conference on Digital Image Processing (2009).
  12. W. Chung, X. Wu, and Y. Xu, "A Real-time Hand Gesture Recognition based on Haar Wavelet Representation",International Conference on Robotics and Biomimetics Bangkok, Thailand, February 21 - 26, 2009.
  13. Asha Thalange, Dr. Shantanu Dixit, "ASL Number Recognition Using Open-finger Distance Feature Measurement Technique", International Journal of Computer Applications (IJCA) December 2014.
  14. S. Nagarajan, T. S. Subashini, " Static Hand Gesture Recognition for Sign Language Alphabets using Edge Oriented Histogram and Multi Class SVM", International Journal of Computer Applications (0975 – 8887) Volume 82 – No4, November 2013.
  15. Rafiqul Zaman Khan , Noor Adnan Ibraheem,"HAND GESTURE RECOGNITION: A LITERATUREREVIEW", International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 3, No. 4, July 2012.
Index Terms

Computer Science
Information Sciences

Keywords

American Sign Language Asl Alphabets Neural Network Static Hand Gesture Recognition Orientation Histogram Statistical Measures