Automatic Sign Language Recognition

Abstract

This thesis proposes two different methods for Isolated sign language recognition, A Hidden Markov Model based classifier that uses trajectory information for classifying a dataset of 40 Arabic Sign Language dataset, It works on relative and scaled trajectories and extracts features from Kinect device, it achieves a real time performance and an accuracy of 99.25% in signer dependent settings and an accuracy of 92.5% in signer independent settings, We also propose a multichannel deep learning model for isolated sign language recognition, The model uses hand trajectories data and leverages hand shape sequential patterns, MobileNet was adapted as a pretrained CNN model for the hand shape features, and a one dimensional Google inception like architecture is proposed for hand trajectory feature extraction along with an