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Structured text understanding on Visually Rich Documents (VRDs) is a crucialpart of Document Intelligence. Due to the complexity of content and layout inVRDs, structured text understanding has been a challenging task. Most existingstudies decoupled this problem into two sub-tasks: entity labeling and entitylinking, which require an entire understanding of the context of documents atboth token and segment levels. However, little work has been concerned with thesolutions that efficiently extract the structured data from different levels.This paper proposes a unified framework named StrucTexT, which is flexible andeffective for handling both sub-tasks. Specifically, based on the transformer,we introduce a segment-token aligned encoder to deal with the entity labelingand entity linking tasks at different levels of granularity. Moreover, wedesign a novel pre-training strategy with three self-supervised tasks to learna richer representation. StrucTexT uses the existing Masked Visual LanguageModeling task and the new Sentence Length Prediction and Paired Boxes Directiontasks to incorporate the multi-modal information across text, image, andlayout. We evaluate our method for structured text understanding atsegment-level and token-level and show it outperforms the state-of-the-artcounterparts with significantly superior performance on the FUNSD, SROIE, andEPHOIE datasets.

StrucTexT: Structured Text Understanding with Multi-Modal Transformers