Applying neural network developments to sign language translation

Neural networks are used to extract relevant features of sign language from video images of a person communicating in American Sign Language or Signed English. The key features are hand motion, hand location with respect to the body, and handshape. A modular design is under way to apply various techniques, including neural networks, in the development of a translation system that will facilitate communication between deaf and hearing people. Signal processing techniques developed for defense-related programs have been adapted and applied to this project. Algorithm development and transition using neural network architectures has been encouraging. The results of the feasibility study for this project are described