Structure-aware Mathematical Expression Recognition with Sequence-Level Modeling

Mathematical expression recognition (MER) aims to convert an image of mathematical expressions into a Latex sequence. In practice, the task of MER is challenging, since 1) the images of mathematical expressions often contain complex structure relationships, e.g., fractions, matrixes, and subscripts; 2) the generated Latex sequences can be very complex and they have to satisfy strict syntax rules. Existing methods, however, often ignore the complex dependence among image regions, resulting in poor feature representation. In addition, they may fail to capture the rigorous relations among different formula symbols as they consider MER as a common language generation task. To address these issues, we propose a Structure-Aware Sequence-Level (SASL) model for MER. First, to better represent and recognize the visual content of formula images, we propose a structure-aware module to capture the relationship among different symbols. Meanwhile, the sequence-level modeling helps the model to concentrate on the generation of entire sequences. To make the problem feasible, we cast the generation problem into a Markov decision process (MDP) and seek to learn a Latex sequence generating policy. Based on MDP, we learn SASL by maximizing the matching score of each image-sequence pair to obtain the generation policy. Extensive experiments on the IM2LATEX-100K dataset verify the effectiveness and superiority of the proposed method.