The week's pick
Random Articles
Reseach Article
ASL Number Recognition using Open-finger Distance Feature Measurement Technique
| 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
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.
References
- 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.
- Myron W. Krueger, Artificial Reality II, Addison-Wesley, Reading, 1991.
- Thomas G. Zimmerman and Jaron Lanier, "A Hand Gesture Interface Device", ACM SIGCHI/GI, pages 189-192, 1987.
- James Davis, and Mubarak Shah, "Recognizing hand gestures", ECCV, pages 331-340, Stockholm, Sweden, May 1994.
- Y. Cui, D. Swets and J. Weng, "Learning-based Hand Sign Recognition using SHOSLIF-M", Proceedings of 5th International Conference on Computer Vision, pp 631—636, Boston, 1995.
- Klimis Symeonidis, "Hand Gesture Recognition Using Neural Networks", Master's Thesis, School of Electronic and Electrical Engineering On August 23, 2000.
- 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.
- 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
- 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.
- E. Stergiopoulou, N. Papamarkos, "Hand gesture recognition using a neural network shape fitting technique", Engineering Applications of Artificial Intelligence Journal (2009).
- U. Rokade, D. Doye, and M. Kokare, "Hand Gesture Recognition Using Object Based Key Frame Selection", International Conference on Digital Image Processing (2009).
- 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.
- R. Rokade, D. Doye, and M. Kokare, "Hand Gesture Recognition by Thinning Method", International Conference on Digital Image Processing (2009).
- Ravikiran J, Kavi Mahesh, Suhas Mahishi, Dheeraj R, Sudheender S, Nitin V Pujari, "Finger Detection for Sign Language Recognition", Proceedings of the International MultiConference of Engineers and Computer Scientists 2009 Vol I IMECS 2009, March 18 - 20, 2009, Hong Kong.
- Asanterabi Malima, Erol Özgür, and Müjdat Çetin, "A Fast Algorithm For Vision-based Hand Gesture Recognition For Robot Control", 2004.
Index Terms
Keywords