International Journal of Engineering and Technology

Normal humans can easily interact and communicate with one another, but the person with hearing and speaking disabilities face problems in communicating with other hearing people without a translator. The Sign Language is a barrier of communication for deaf and dumb people. People with hearing and speaking disability are highly dependent on non-verbal form of communication that involves hand gesture. This is the reason that the implementation of a system that recognize the sign language would have a significant benefit impact on dumb - deaf people. In this paper, a method is proposed for the automatic recognition of the finger spelling in the Indian sign language. Here, the sign in the form of gestures is given as an input to the system. Further various steps are performed on the input sign image. Firstly segmentation phase is performed based on the skin color so as to detect the shape of the sign. The detected region is then transformed into binary image. Later, the Euclidean distance transformation is applied on the obtained binary image. Row and column projection is applied on the distance transformed image. For feature extraction central moments along with HU’s moments are used. For classification, neural network and SVM are used.