Counting the number of words and lines read by fusing eye tracking and character recognition data: A Bayes factor approach

Abstract

Counting the number of words and lines that a user reads is important for many educational purposes – e.g., the reading speed is a key factor to improve learning, intelligent systems can suggest text that must be read to achieve a determined learning objective. The eye tracking technology is commonly used to analyze the user reading habits. Counting the number of read words could be hard when the readings are obtained from imprecise eye tracking data – e.g., eye tracking calibration difficulties. Approaches that find patterns from saccades and fixations usually fail to solve the problem in such conditions. This paper introduces the Cowl approach, which deals with the imprecision problem by associating the eye tracking data with points obtained from character recognition. To detect text lines truly read, the problem is stated as one of merging two hypothetical lines and it is solved by a Bayesian approach. Tests show that the proposed approach shows high performance, reaching average precision rates up to 0.866 for recall 0.976 – in the case of text with different orientations.