Continuous recognition and visualization of pen strokes and touch-screen gestures

Abstract

We present a technique that enables continuous recognition and visualization of pen strokes and touch-screen gestures. We describe an incremental recognition algorithm that provides probability distributions over template classes as a function of users' partial or complete stroke articulations. We show that this algorithm can predict users' intended template classes with high accuracy on several different datasets. We use the algorithm to design two new visualizations that reveal various aspects of the recognition process to users. We then demonstrate how these visualizations can help users to understand how the recognition process interprets their input and how interactions between different template classes affect recognition outcomes.