The week's pick
Random Articles
Reseach Article
Static Hand Gesture Recognition for Sign Language Alphabets using Edge Oriented Histogram and Multi Class SVM
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 82 - Number 4 |
| Year of Publication: 2013 |
| Authors: S. Nagarajan, T. S. Subashini |
10.5120/14106-2145
|
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. 82, 4 ( November 2013), 28-35. DOI=10.5120/14106-2145
Abstract
In recent years, enormous research is progressing in the field of Computer Vision and Human Computer Interaction where hand gestures play a vital role. Hand gestures are more powerful means of communication for hearing impaired when they communicate to the normal people everywhere in day to day life. As the normal people find little difficulty in recognizing and interpreting the meaning of sign language expressed by the hearing impaired, it is inevitable to have an interpreter for translation of sign language. To overcome this difficulty, an automatic hand gesture recognition system which translates the sign language into text needs to be developed. In this paper, a static hand gesture recognition system for American Sign Language using Edge Oriented Histogram (EOH) features and multiclass SVM is proposed. The edge histogram count of input sign language alphabets is extracted as the features and applied to a multiclass SVM for classification. The average accuracy of the system is compared with different number of features and the experimental findings demonstrate that the proposed method gives a success rate of 93. 75%.
References
- Singha. J, and Das. K, "Hand Gesture Recognition based on Karhunen-Loeve Transform", Mobile and Embedded Technology International Conference (MECON), January 17-18, 2013, pp 365-371.
- Joyeeta Singha, and Karen Das, "Recognition of Indian Sign Language in Live Video", International Journal of Computer Applications, Vol. 70, No. 19, May 2013.
- T. Kapsciinski and M. Wysocki, "Hand Gesture Recognition for Man-Machine interaction", Second Workshop on Robot Motion and Control, October 18-20, 2001, pp. 91-96.
- I. G. Incertis, J. G. G. Bermejo, and E. Z. Casanova, "Hand Gesture Recognition for Deaf People Interfacing", The 18th International Conference on Pattern Recognition (ICPR), 2006.
- Nasser H. Dardas, Nicolas D. Georganas, "Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques", IEEE Transactions on Instrumentation and Measurement, Vol. 60, No. 11, November 2011.
- Jayashree R. Pansare, Sharavan H. Gawande, Maya Ingle, "Real-Time Static Hand Gesture Recognition for American Sign Language (ASL) in Complex Background", Journal of Signal and Information Processing, 2012, Vol. 3, pp 364-367.
- Md. AtiqurRahman, Ahsan-Ul-Ambia,Md. Aktaruzzaman, "Recognition of Static Hand Gestures of Alphabet in ASL", IJCIT, Vol. 2, Issue 1, 2011.
- Rajesh Mapari, Dr. Govind Kharat, "Hand Gesture Recognition using Neural Network", International Journal of Computer Science and Network, Vol. 1, Issue 6, December 2012.
- A. A. Randive, H. B. Mali, S. D. Lokhande, "Hand Gesture Segmentation", International Journal of Computer Technology and Electronics Engineering, Vol. 2, Issue 3, June2012.
- Vaishali S. Kulkarni, Dr. S. D. Lokhande, "Appearance Based Recognition of American Sign Language Using Gesture Segmentation", International Journal of Computer Science and Engineering, Vol. 2, No. 3, 2010, pp 560-565.
- Deval G. Patel, "Point Pattern Matching algorithm for recognition of 36 ASL gestures", International Journal of Science and Modern Engineering, Vol. 1, Issue 7, June 2013.
- Joyeeta Singha, Karen Das, "Indian Sign Language Recognition Using Eigen Value Weighted Euclidean Distance Based Classification Technique", International Journal of Advanced Computer Science and Applications, Vol. 4, No. 2, 2013.
- D. Y. Huang, W. C. Hu, and S. H. Chang, "Vision based Hand Gesture Recognition Using PCA+Gabor filters and SVM", IEEE Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, pp 1-4.
- Ali Karami, Bahman Zanj, Azadeh Kiani Sarkalesh, "Persian sign language (PSL) recognition using wavelet Transform and neural Networks", ELSEVIER Journal of Expert Systems with Applications 38, 2011, 2661-2667.
- Manigandan. M, I. M. Jackin, "Wireless Vision based Mobile Robot control using Hand Gesture Recognition through Perceptual Color Space", IEEE International Conference on Advances in Computer Engineering, 2010, pp. 95-99.
- S. Saengsri, V. Niennattrakul, and C. A. Ratana mahatana, "TFRS: Thai Finger-spelling Sign Language Recognition System", IEEE, 2012, pp 457-462.
- ASL Dataset. http://personal. ee. surrey. ac. uk/
- Rafael C. Gonzalez, Richard E. Woods, "Digital Image Processing", Second Edition, 2005.
- Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, "Digital Image Processing Using MATLAB", Pearson Education, 2007.
- V. Vapnik, "Statistical Learning Theory", Wiley, New York, 1998.
- SVM-Support Vector Machines: www. dtreg. com/SVM. htm
- Courant, Richard, and David Hilbert. "Methods of Mathematical Physics, Vol. I. " Physics Today 7. 5 (1954): 17-17.
- Marina Sokolova, Nathalic Japakowicz, Stan Szpakowicz, "Beyond Accuracy, F-Score and ROC: a family of Discriminant measures for performance evaluation", 2006.
Index Terms
Keywords