Comparative Analysis of Movement and Tracking Techniques for Indian Sign Language Recognition

Sign Language is considered as a way of communication for hearing handicapped persons. We can make the communication of deaf people easier by building a translation system of this language. To realize these systems, the identification of words and gestures in sign language is very important. Indian Sign Language (ISL) is used in major parts of India that includes gestures. Most of the gestures include movements of a part of body. Here, in this paper, the focus is to track the movement of hand, identifying its shape and direction of motion. The tracking techniques are compared on some factors and analysis is done. Preprocessing for extracting the region of interest (a hand) is done on image sequences. Tracking is done through Mean-shift and Kalman filter. The performance of the above mentioned algorithms are compared on the basis of precision, tracking time, affect of velocity change and recognition. Different shape based features are extracted based on different region based shape models. The preprocessing and feature extraction is done in MATLAB. After extracting these features are applied as input to a classifier. Classification is done in WEKA. Performance of the system is analyzed by identification of hand shape with direction.