Document AI: A Comparative Study of Transformer-Based, Graph-Based
  Models, and Convolutional Neural Networks For Document Layout Analysis
Sotirios Kastanas Shaomu Tan Yi He
Document AI aims to automatically analyze documents by leveraging natural
language processing and computer vision techniques. One of the major tasks of
Document AI is document layout analysis, which structures document pages by
interpreting the content and spatial relationships of layout, image, and text.
This task can be image-centric, wherein the aim is to identify and label
various regions such as authors and paragraphs, or text-centric, where the
focus is on classifying individual words in a document. Although there are
increasingly sophisticated methods for improving layout analysis, doubts remain
about the extent to which their findings can be generalized to a broader
context. Specifically, prior work developed systems based on very different
architectures, such as transformer-based, graph-based, and CNNs. However, no
work has mentioned the effectiveness of these models in a comparative analysis.
Moreover, while language-independent Document AI models capable of knowledge
transfer have been developed, it remains to be investigated to what degree they
can effectively transfer knowledge. In this study, we aim to fill these gaps by
conducting a comparative evaluation of state-of-the-art models in document
layout analysis and investigating the potential of cross-lingual layout
analysis by utilizing machine translation techniques.