  Document layout analysis has a wide range of requirements across various
domains, languages, and business scenarios. However, most current
state-of-the-art algorithms are language-dependent, with architectures that
rely on transformer encoders or language-specific text encoders, such as BERT,
for feature extraction. These approaches are limited in their ability to handle
very long documents due to input sequence length constraints and are closely
tied to language-specific tokenizers. Additionally, training a cross-language
text encoder can be challenging due to the lack of labeled multilingual
document datasets that consider privacy. Furthermore, some layout tasks require
a clean separation between different layout components without overlap, which
can be difficult for image segmentation-based algorithms to achieve. In this
paper, we present Paragraph2Graph, a language-independent graph neural network
(GNN)-based model that achieves competitive results on common document layout
datasets while being adaptable to business scenarios with strict separation.
With only 19.95 million parameters, our model is suitable for industrial
applications, particularly in multi-language scenarios.
