APPROACHES AND SOLUTIONS FOR SIGN LANGUAGE RECOGNITION PROBLEM
DOI:
https://doi.org/10.17770/sie2018vol1.3082Keywords:
Sign language recognition, artificial neural networks, Latvian sign languageAbstract
The goal of the paper is reviewing several aspects of Sign Language Recognition problems focusing on Artificial Neural Network approach. The lack of automated Latvian Sign Language has identified and proposals of how to develop such a system have made. Tha authors use analytical, statistical methods as well as practical experiments with neural network software. The main results of the paper are description of main Sign Language Recognition problem solving methods with Artificial Neural Networks and directions of future work based on authors’ previous expertise.Downloads
References
Cooper H., Holt B., & Bowden R. (2011) Sign Language Recognition, Chapter in Visual Analysis of Humans: Looking at People, Springer.
Dogic S., & Karli G. (2014). Sign Language Recognition using Neural Networks. TEM Journal 3(4), 296-301.
Fausett L. (1994). Fundamentals of Neural Networks. Architectures, algorithms and applications, Prentice Hall.
Kang B., Tripathi S., & Nguyen T. (2015). Real-time Sign Language Fingerspelling Recognition using Convolutional Neural Networks from Depth map. In: 3rd IAPR Asian Conference on Pattern Recognition, Kuala Lumpur, Malaysia.
Konar A. (2005). Computational intelligence: principles, techniques and applications. - Springer-Verlag, London.
Mekala P. etc. (2013). Gesture Recognition Using Neural Networks Based on HW/SW Cosimulation Platform. Advances in Software Engineering, Volume 2013.
Mekala P. etc. (2011). Real-time Sign Language Recognition based on Neural Network Architecture. IEEE 43rd Southeastern Symposium on System Theory, 195-199, Auburn, USA.
Rojas, R. (1996). Neural networks. A systematic approach, Springer, Berlin.
The Latvian Sign Language Development Department. Retrieved from http://rc.lns.lv/index.php .
Zorins ,A. (2007). Improvement possibilities of interval value prediction using Kohonen neural networks. Riga Technical University conference proceedings, Vol. 31, 8.-16.
Zorins, A. (2009). Retail turnover prediction using modular artificial neural networks. Proceedings of 7th International Scientific Practical Conference “Environment. Technology. Resources”, Rezekne, Latvia.