Utilizing Consistency Context for Handwritten Mathematical Expression Recognition

This paper presents a rule-based approach that utilizes some types of contextual information to improve the accuracy of handwritten mathematical expression(ME) recognition. Mining context from corpus is not practical for ME recognition due to the complexity originated from 2-D nature of MEs. For practicality, we identify typical types of consistencies that are often found in customary usage and general patterns in MEs. We aim to increase these consistencies in recognition results by correcting symbol labels and/or spatial relations among symbols. Such consistencies are easily encoded as condition-action pairs. Preliminary interpretations generated by the base recognizer are reordered by increasing or decreasing scores by the rules. Although our approach is not complete, it easily implements even global context among distant symbols. Experimental results show that our approach is useful to increase the accuracy of handwritten ME recognition.