Sign Language Recognition Using Leap Motion

A Support Vector Machine Approach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9454)


Several million people around the world use signs as their main mean of communication. The advances in technologies to recognize such signs will make possible the computer supported interpretation of sign languages. There are more than 137 different sign language around the world; therefore, a system that interprets those languages could be beneficial to all, including the Deaf Community. This paper presents a system based on a hand tracking device called Leap Motion, used for signs recognition. The system uses a Support Vector Machine for sign classification. We performed three different evaluations of our system with over 24 people.


American Sign Language Leap Motion Support Vector Machine Automatic sign language recognition 



This work was partially supported by the Escuela de Ciencias de la Computación e Informática at Universidad de Costa Rica (ECCI-UCR) grand No. 320-B5-291, by Centro de Investigaciones en Tecnologías de la Información y Comunicación de la Universidad de Costa Rica (CITIC-UCR), and by Ministerio de Ciencia, Tecnología y Telecomunicaciones (MICITT) and Consejo Nacional para Investigaciones Científicas y Tecnológicas (CONICIT) of the Government of Costa Rica.


  1. 1.
    Lewis, P., Simons, G., Fennig, C.: Ethnologue: Languages of the World. SIL International, Dallas (2009)Google Scholar
  2. 2.
    Hanson, V.: Computing technologies for deaf and hard of hearing users. In: Sears, A., Jacko, J. (eds.) Human Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, pp. 886–893. CRC Press, Boca Raton (2012)Google Scholar
  3. 3.
  4. 4.
    Guna, J., Jakus, G., Pogačnik, M., Tomažič, S., Sodnik, J.: An analysis of the precision and reliability of the leap motion sensor and its suitability for static and dynamic tracking. Sensors 14(2), 3702–3720 (2014)CrossRefGoogle Scholar
  5. 5.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  6. 6.
    Caridakis, G., Asteriadis, S., Karpouzis, K.: Non-manual cues in automatic sign language recognition. Pers. Ubiquit. Comput. 18(1), 37–46 (2014)CrossRefGoogle Scholar
  7. 7.
    Zafrulla, Z., Brashear, H., Starner, T., Hamilton, H., Presti, P.: American Sign Language recognition with the kinect. In: Proceedings of the 13th International Conference on Multimodal Interfaces, pp. 279–286. ACM, New York (2011)Google Scholar
  8. 8.
    Sun, C., Zhang, T., Xu, C.: Latent support vector machine modeling for sign language recognition with kinect. ACM Trans. Intell. Syst. Technol. 6(2), 1–20 (2015)CrossRefGoogle Scholar
  9. 9.
    Potter, L., Araullo, J., Carter, L.: The leap motion controller: a view on sign language. In: Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration, pp. 175–178. ACM. New York (2013)Google Scholar
  10. 10.
    Chuan, C., Regina, E., Guardino, C.: American Sign Language recognition using leap motion sensor. In: International Conference on Machine Learning and Applications, pp. 541–544. IEEE Press, New York (2014)Google Scholar
  11. 11.
    Knerr, S., Personnaz, L., Dreyfus, G.: Single-layer learning revisited: a stepwise procedure for building and training a neural network. Neurocomputing 68, 41–50 (1990)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Scikit-learn: machine learning in python.
  13. 13.
    Mohandes, M., Aliyu, S., Deriche, M.: Arabic sign language recognition using the leap motion controller. In: IEEE International Symposium on Industrial Electronics, pp. 960–965. IEEE Press, New York (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Escuela de Ciencias de La Computación E InformáticaUniversidad de Costa RicaSan PedroCosta Rica
  2. 2.Centro de Investigaciones En Tecnologías de La Información Y ComunicaciónUniversidad de Costa RicaSan PedroCosta Rica

Personalised recommendations