A phoneme based sign language recognition system using 2D moment invariant interleaving feature and Neural Network

Sign language recognition is one of the most promising sub-fields in gesture recognition research. A sign language is a language which, instead of acoustically conveyed sound patterns, uses visually transmitted sign patterns. Sign languages are commonly developed for hearing impaired communities, which can include interpreters, friends and families of hearing impaired people as well as people who are hard of hearing themselves. Developing a sign language recognition system will help the hearing impaired to communicate more fluently with the normal people. A simple sign language recognition system employing skin color segmentation and Neural Network has been developed. A simple segmentation process is carried out to separate the right and left hand regions from the image frame and in the preprocessing stage a simple vertical interleaving method has been proposed to reduce the size of the image. The 2D moment invariant features of the right and left hand interleaved image is obtained as features. Using the interleaved 2D-moment Invariant features, a simple neural network model has been developed. The system has been implemented and tested for its validity. Experimental results show that the system has a recognition rate of 92.58%.