Review of Data Preprocessing Methods for Sign Language Recognition Systems based on Artificial Neural Networks

Open access

Abstract

The article presents an introductory analysis of relevant research topic for Latvian deaf society, which is the development of the Latvian Sign Language Recognition System. More specifically the data preprocessing methods are discussed in the paper and several approaches are shown with a focus on systems based on artificial neural networks, which are one of the most successful solutions for sign language recognition task.

References

  • [1] Spread the sign. [Online]. Available: https://www.spreadthesign.com/us/aboutus/ [Accessed: May 11, 2016].

  • [2] The Latvian Sign Language Development Department. [Online]. Available: http://rc.lns.lv/index.php [Accessed: May 11, 2016].

  • [3] A. Zorins and P. Grabusts, “Review of sign language recognition systems based on artificial neural networks,” in MENDEL 2016 conference proceedings, Brno, Czech Republic, 2016.

  • [4] The Leap Motion Store. [Online]. Available: http://store-us.leapmotion.com/ [Accessed: May 11, 2016].

  • [5] L. Fausett, Fundamentals of Neural Networks. Architectures, algorithms and applications, Upper Saddle River, NJ: Prentice-Hall, 1994.

  • [6] R. Rojas, Neural networks. A systematic approach, Berlin, Germany: Springer, 1996.

  • [7] H. Cooper, B. Holt and R. Bowden, “Sign Language Recognition,” in Visual Analysis of Humans: Looking at People, London, UK: Springer, 2011, pp. 539-562. https://doi.org/10.1007/978-0-85729-997-0_27

  • [8] Myo Gesture Control Armband. [Online]. Available: https://www.amazon.co.uk/MYO-MYO-00002-001-Gesture-Control-Armband/dp/B00O66E58M/ref=sr_1_1?ie=UTF8&qid=1462967348&sr=8-1&keywords=myo [Accessed: May 11, 2016]

  • [9] S. Đogić and G. Karli, “Sign Language Recognition using Neural Networks,” TEM Journal, vol. 3, issue 4, pp. 296-301, 2014.

  • [10] B. Kang, S. Tripathi and T. Nguyen, “Real-time Sign Language Fingerspelling Recognition using Convolutional Neural Networks from Depth map,” in 3rd IAPR Asian Conference on Pattern Recognition, Kuala Lumpur, Malaysia, 2015. https://doi.org/10.1109/acpr.2015.7486481

  • [11] P. Mekala et al., “Gesture Recognition Using Neural Networks Based on HW/SW Cosimulation Platform,” Advances in Software Engineering, vol. 2013, 2013. https://doi.org/10.1155/2013/707248

  • [12] P. Mekala et al., “Real-time Sign Language Recognition based on Neural Network Architecture,” in IEEE 43rd Southeastern Symposium on System Theory, Auburn, AL, 2011, pp. 195-199. https://doi.org/10.1109/SSST.2011.5753805

  • [13] E. Keogh and M. Pazzani, “An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback,” in Proceedings of the Fourth International Conference of Knowledge Discovery and Data Mining, 1998, pp. 239-241.

  • [14] A. Zorins, “Data preprocessing methods for interval based neural network prediction,” in Proceedings of 7th International Scientific Practical Conference “Environment. Technology. Resources,” Rezekne, Latvia, 2007.

Information Technology and Management Science

The Journal of Riga Technical University

Journal Information

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 11 11 11
PDF Downloads 4 4 4