Online Handwritten Mathematical Symbol Segmentation and Recognition with Bidirectional Context

Discriminating ambiguous symbols in online handwritten mathematical expression is difficult without context. We propose a Bidirectional Recurrent Neural Network for segmenting and classifying online handwritten mathematical symbols. The context from forward and backward directions helps the classification model discriminate ambiguous symbols and improve recognition rates. The classification model is integrated into the Stochastic Context-Free Grammar recognition system for recognizing mathematical expressions. We show the effectiveness of the approach for improving symbol classification and segmentation on the CROHME 2016 dataset.