A Graphical Approach to Document Layout Analysis
Jilin Wang Michael Krumdick Baojia Tong Hamima Halim Maxim Sokolov Vadym Barda Delphine Vendryes Chris Tanner
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.