Recognition of Printed Mathematical Expressions Using Two-Dimensional Stochastic Context-Free Grammars

In this work, a system for recognition of printed mathematical expressions has been developed. Hence, a statistical framework based on two-dimensional stochastic context-free grammars has been defined. This formal framework allows to jointly tackle the segmentation, symbol recognition and structural analysis of a mathematical expression by computing its most probable parsing. In order to test this approach a reproducible and comparable experiment has been carried out over a large publicly available (InftyCDB-1) database. Results are reported using a well-defined global dissimilitude measure. Experimental results show that this technique is able to properly recognize mathematical expressions, and that the structural information improves the symbol recognition step.