Recognizing the layout of unstructured digital documents is crucial when
parsing the documents into the structured, machine-readable format for
downstream applications. Recent studies in Document Layout Analysis usually
rely on computer vision models to understand documents while ignoring other
information, such as context information or relation of document components,
which are vital to capture. Our Doc-GCN presents an effective way to harmonize
and integrate heterogeneous aspects for Document Layout Analysis. We first
construct graphs to explicitly describe four main aspects, including syntactic,
semantic, density, and appearance/visual information. Then, we apply graph
convolutional networks for representing each aspect of information and use
pooling to integrate them. Finally, we aggregate each aspect and feed them into
2-layer MLPs for document layout component classification. Our Doc-GCN achieves
new state-of-the-art results in three widely used DLA datasets.

我们提出了一种新的文档布局分析方法，即 Doc-GCN ，该方法通过构建图形来描述文档的固有特征并将这些信息应用于图卷积网络中。最终结果表明，我们的方法在三个常用的 DLA 数据集中取得了新的最佳结果。