Recognition of on-line handwritten mathematical symbols has been tackled using different methods, but the recognition rates achieved until now still leave room for improvement. Many of the published approaches are based on hidden Markov models, and some of them use off-line information extracted from the on-line data. In this paper, we present a set of hybrid features that combine both on-line and off-line information. Lately, recurrent neural networks have demonstrated to obtain good results and they have outperformed hidden Markov models in several sequence learning tasks, including handwritten text recognition. Hence, we also studied a state-of-the-art recurrent neural network classifier and we compared its performance with a classifier based on hidden Markov models. Experiments using a large public database showed that both the new proposed features and recurrent neural network classifier improved significantly the classification results.

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.