Global Context-Based Network with Transformer for Image2latex

Nuo Pang, Chun Yang, Xiaobin Zhu, Jixuan Li, Xu-Cheng Yin

Abstract: Image2latex usually means converts mathematical formulas in images into latex markup. It is a very challenging job due to the complex two-dimensional structure, variant scales of input, and very long representation sequence. Many researchers use encoder-decoder based model to solve this task and achieved good results. However, these methods don't make full use of the structure and position information of the formula. To solve this problem, we propose a global context-based network with transformer that can (1) learn a more powerful and robust intermediate representation via aggregating global features and (2) encode position information explicitly and (3) learn latent dependencies between symbols by using self-attention mechanism. The experimental results on the dataset IM2LATEX-100K demonstrate the effectiveness of our method.