  Document layout analysis (DLA) is crucial for understanding the physical
layout and logical structure of documents, serving information retrieval,
document summarization, knowledge extraction, etc. However, previous studies
have typically used separate models to address individual sub-tasks within DLA,
including table/figure detection, text region detection, logical role
classification, and reading order prediction. In this work, we propose an
end-to-end transformer-based approach for document layout analysis, called
DLAFormer, which integrates all these sub-tasks into a single model. To achieve
this, we treat various DLA sub-tasks (such as text region detection, logical
role classification, and reading order prediction) as relation prediction
problems and consolidate these relation prediction labels into a unified label
space, allowing a unified relation prediction module to handle multiple tasks
concurrently. Additionally, we introduce a novel set of type-wise queries to
enhance the physical meaning of content queries in DETR. Moreover, we adopt a
coarse-to-fine strategy to accurately identify graphical page objects.
Experimental results demonstrate that our proposed DLAFormer outperforms
previous approaches that employ multi-branch or multi-stage architectures for
multiple tasks on two document layout analysis benchmarks, DocLayNet and
Comp-HRDoc.
