Document layout analysis (DLA) is the task of detecting the distinct,
semantic content within a document and correctly classifying these items into
an appropriate category (e.g., text, title, figure). DLA pipelines enable users
to convert documents into structured machine-readable formats that can then be
used for many useful downstream tasks. Most existing state-of-the-art (SOTA)
DLA models represent documents as images, discarding the rich metadata
available in electronically generated PDFs. Directly leveraging this metadata,
we represent each PDF page as a structured graph and frame the DLA problem as a
graph segmentation and classification problem. We introduce the Graph-based
Layout Analysis Model (GLAM), a lightweight graph neural network competitive
with SOTA models on two challenging DLA datasets - while being an order of
magnitude smaller than existing models. In particular, the 4-million parameter
GLAM model outperforms the leading 140M+ parameter computer vision-based model
on 5 of the 11 classes on the DocLayNet dataset. A simple ensemble of these two
models achieves a new state-of-the-art on DocLayNet, increasing mAP from 76.8
to 80.8. Overall, GLAM is over 5 times more efficient than SOTA models, making
GLAM a favorable engineering choice for DLA tasks.

文档布局分析是检测文档中不同的语义内容并正确分类到适当类别（如文本、标题、图表）的任务。本研究采用基于图的布局分析模型（GLAM），将每个 PDF 页面表示为结构化图，并将布局分析问题作为图分割和分类问题进行。与现有模型相比，GLAM 既能在两个具有挑战性的数据集上与现有模型相媲美，又比现有模型小一个数量级。