LATVIAN SIGN LANGUAGE RECOGNITION CLASSIFICATION POSSIBILITIES

Authors

  • Aleksejs Zorins Rezekne Academy of Technology
  • Peter Grabusts Rezekne Academy of Technology

DOI:

https://doi.org/10.17770/etr2017vol2.2653

Keywords:

artificial neural networks, centre of gravity, classification, hand gesture, sign language recognition

Abstract

There is a lack of automated sign language recognition system in Latvia while many other countries have been already equipped with such a system. Latvian deaf society requires support of such a system which would allow people with special needs to enhance their communication in governmental and public places. The aim of this paper is to recognize Latvian sign language alphabet using classification approach with artificial neural networks, which is a first step in developing integral system of Latvian Sign Language recognition. Communication in our daily life is generally vocal, but body language has its own significance. It has many areas of application like sign languages are used for various purposes and in case of people who are deaf and dumb, sign language plays an important role. Gestures are the very first form of communication. The paper presents Sign Language Recognition possibilities with centre of gravity method. So this area influenced us very much to carry on the further work related to hand gesture classification and sign’s clustering.

Downloads

Download data is not yet available.

References

The Latvian Sign Language Development Department. [Online]. Available: http://zimjuvaloda.lv/lv/alphabet/daktilo-zimju-alfabets [Accessed: March 23,2017] (in Latvian).

The Leap Motion Store. [Online]. Available: https://store-eur.leapmotion.com/products/leap-motion-controller [Accessed: March 23, 2017].

R. Rojas, Neural networks. A systematic approach. Springer, Berlin, 1996.

L. Fausett, Fundamentals of Neural Networks. Architectures, algorithms and applications. Prentice Hall, Inc., 1994, pp.169-187.

A. Zorins and P. Grabusts, "Review of Data Preprocessing Methods for Sign Language Recognition Systems based on Artificial Neural Networks," International Scientific Journal of Riga Technical University, vol. "Information Technology and Management Science", December, 2016, vol. 19, pp. 98–103.

A. Konar, Computational intelligence: principles, techniques and applications. Springer-Verlag, London, 2005.

A. Zorins, Improvement possibilities of interval value prediction using Kohonen neural networks. Riga Technical University conference proceedings, Vol. 31, 8.-16. Riga, Latvia, 2007.

P. K. Pisharady, M. Saerbeck, "Recent methods and databases in vision-based hand gesture recognition: A review," Computer Vision and Image Understanding 141:152-165, December 2015.

J.R. Pansare, S.H. Gawande, M. Ingle, "Real-time static hand gesture recognition for American Sign Language in complex background," Journal of Signal and Information Processing,V.3., pp. 364-367, 2012. http://file.scirp.org/pdf/JSIP20120300010_94521693.pdf

C.V. Ng, S. Ranganath, "Real-time gesture recognition and application, Image and Vision Computing," 20, pp. 993-1007, 2002.

M.K. Ghose, A. Pradhan, "A hand gesture recognition using feature extraction," International Journal of Current Engineering and Technology, 2012.

Downloads

Published

2017-06-15

How to Cite

[1]
A. Zorins and P. Grabusts, “LATVIAN SIGN LANGUAGE RECOGNITION CLASSIFICATION POSSIBILITIES”, ETR, vol. 2, pp. 185–188, Jun. 2017, doi: 10.17770/etr2017vol2.2653.