Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs

TL;DR

This paper benchmarks various Graph Neural Network architectures for detailed layout classification of digital documents, demonstrating the effectiveness of local relationships and multimodal fusion in improving accuracy.

Contribution

It introduces two graph construction methods and evaluates multimodal GNN frameworks on a large, diverse dataset of public affairs documents, highlighting the best configurations.

Findings

GraphSAGE with k-closest-neighbor graph achieves top accuracy.

Dual-branch multimodal GNN outperforms single-modality models.

Local layout relationships and multimodal fusion are crucial for document analysis.

Abstract

The automatic analysis of document layouts in digital-born PDF documents remains a challenging problem due to the heterogeneous arrangement of textual and nontextual elements and the imprecision of the textual metadata in the Portable Document Format. In this work, we benchmark Graph Neural Network (GNN) architectures for the task of fine-grained layout classification of text blocks from digital native documents. We introduce two graph construction structures: a k-closest-neighbor graph and a fully connected graph, and generate node features via pre-trained text and vision models, thus avoiding manual feature engineering. Three experimental frameworks are evaluated: single-modality (text or visual), concatenated multimodal, and dual-branch multimodal. We evaluated four foundational GNN models and compared them with the baseline. Our experiments are specifically conducted on a rich dataset of public affairs documents that includes more than 20 sources (e.g., regional and national-level official gazettes), 37K PDF documents, with 441K pages in total. Our results demonstrate that GraphSAGE operating on the k-closest-neighbor graph in a dual-branch configuration achieves the highest per-class and overall accuracy, outperforming the baseline in some sources. These findings confirm the importance of local layout relationships and multimodal fusion exploited through GNNs for the analysis of native digital document layouts.