A Conditional Random Field Based Indian Sign Language Recognition System under Complex Background

Sign languages are natural languages that use different means of expression for communication in everyday life. Automatic sign language recognition has a significant impact on human society as it can provide an opportunity for the deaf to communicate with non-signing people without the need for an interpreter. In this paper, we present a Conditional Random Field (CRF) based Indian Sign Language (ISL) recognition system which is effective under complex background using a novel set of features. Hand segmentation is the most crucial step in every hand gesture recognition system since if we get better segmented output, better recognition rates can be achieved. The proposed system also includes efficient and robust hand segmentation and tracking algorithm to achieve better recognition rates.