A Real-Time Recognition System for Handwritten Mathematics: Backtracking and Relationship Discovery

Abstract

This paper describes a real-time approach for handwritten, mathematical expression recognition. Since users can view the output of the system after sketching each stroke, it is useful to retain as much information as possible from previous increments of an expression. However, if subsequent input results in an unordered or multi-stroke symbol, it can have adverse effects on a previously identified expression. Rather than reprocess the entire expression, it would be more beneficial to only reprocess a sub expression. To this end we have developed a backtracking technique, which can revert back to the expression discovered before this sub expression. An added benefit of this technique is that it simplifies other processes of recognition, such as relationship discovery.