• DocumentCode
    618448
  • Title

    Artificial neural network based method for Indian sign language recognition

  • Author

    Adithya, V. ; Vinod, P.R. ; Gopalakrishnan, Uma

  • Author_Institution
    Dept. of Comput. Eng., Coll. of Eng. Chengannur, Chengannur, India
  • fYear
    2013
  • fDate
    11-12 April 2013
  • Firstpage
    1080
  • Lastpage
    1085
  • Abstract
    Sign Language is a language which uses hand gestures, facial expressions and body movements for communication. A sign language consists of either word level signs or fingerspelling. It is the only communication mean for the deaf-dumb community. But the hearing people never try to learn the sign language. So the deaf people cannot interact with the normal people without a sign language interpreter. This causes the isolation of deaf people in the society. So a system that automatically recognizes the sign language is necessary. The implementation of such a system provides a platform for the interaction of hearing disabled people with the rest of the world without an interpreter. In this paper, we propose a method for the automatic recognition of fingerspelling in Indian sign language. The proposed method uses digital image processing techniques and artificial neural network for recognizing different signs.
  • Keywords
    handicapped aids; neural nets; sign language recognition; Indian sign language recognition; artificial neural network; deaf-dumb community; facial expression; fingerspelling automatic recognition; hand gesture; Assistive technology; Feature extraction; Gesture recognition; Image color analysis; Shape; Transforms; Vectors; Artificial neural network; Central moments; Distance transform; Fourier descriptors; Hand segmentation; Indian sign language; Projections;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information & Communication Technologies (ICT), 2013 IEEE Conference on
  • Conference_Location
    JeJu Island
  • Print_ISBN
    978-1-4673-5759-3
  • Type

    conf

  • DOI
    10.1109/CICT.2013.6558259
  • Filename
    6558259