Ambiguity and Constraint in Mathematical Expression Recognition

Abstract

The problem of recognizing mathematical expressions differs significantly from the recognition of standard prose. While in prose significant constraints can be put on the interpretation of a character by the characters immediately preceding and following it, few such sim-ple constraints are present in a mathematical expres-sion. In order to make the problem tractable, effective methods of recognizing mathematical expressions will need to put intelligent constraints on the possible in-terpretations. The authors present preliminary results on a system for the recognition of both handwritten and typeset mathematical expressions. While previous systems perform character recognition out of context, the current system maintains ambiguity of the char-acters until context can be used to disambiguate the interpretatiom In addition, the system limits the num-ber of potentially valid interpretations by decomposing the expressions into a sequence of compatible convex regions. The system uses A-star to search for the best possible interpretation of an expression. We provide a new lower bound estimate on the cost to goal that improves performance significantly.