Algorithms for Reading Line Classification

Eye-Tracking has been emerging as a useful tool in human-computer interaction. However, the state of the art in eye-tracking applications suffers from a significant amount of measurement noise. Also, the inherent nature of the eye-gaze movement adds to the difficulty of obtaining valuable information from eye-gaze measurements. In this paper, a novel classification approach is proposed to classify the lines being read based on eye-gaze measurements. The proposed approach consists of a novel Kalman smoother-based preprocessing procedure to separate eye-gaze data corresponding to different text lines and to reduce variance. The preprocessed data is then used to train two different classifiers, one based on Gaussian discriminants and the other based on support vector machines. The resulting line-classification approach is shown to be superior in performance compared to other recent approaches.