Unbiased Evaluation of Handwritten Mathematical Expression Recognition

Several approaches have been proposed to tackle the problem of mathematical expression recognition, and automatic methods for performance evaluation are required. Mathematical expressions are usually encoded as a LaTeX string or a tree (MathML) for evaluation purpose, but these formats do not enforce uniqueness. Consequently, given that there can be several representations syntactically different but semantically equivalent, the automatic performance evaluation of mathematical expressions can be biased. Given a mathematical expression recognition tree and its ground-truth tree, the error is usually computed by comparing them. In this paper we propose to obtain a new tree, equivalent to the ground-truth tree, according to the model representation criteria. Then, we can compute an error by comparing the recognized tree with the obtained by using the model, both with the same bias. Several experiments were carried out in order to evaluate this approach and results showed that representation criteria had a significative effect in the evaluation results.