Sign Language Recognition Using Principal Component Analysis

Sign language recognition is an important research problem for enabling communication with hearing impaired people. This paper presents principal component analysis which is a fast and efficient technique for recognition of sign gestures from video stream. Capturing of images from live video can be done using webcam or an android device. In this proposed technique we capture 3 frames per second from video stream. After that we compare three continuous frames to know the frame, containing static posture shown by hand. This static posture is recognized as a sign gesture. Now it is matched with stored gesture database to know its meaning. This system has been tested and developed successfully in a real time environment with approx 90% matching rate.