Classification of On-Line Mathematical Symbols with Hybrid Features and Recurrent Neural Networks

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.