Papers › Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks

Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks

23 Aug 2022arXiv:2208.11168archive 2025-07-28

Andrea Gemelli, Sanket Biswas, Enrico Civitelli, Josep Lladós, Simone Marinai

Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection. Our code is freely accessible on https://github.com/andreagemelli/doc2graph.

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Tasks

Document Layout AnalysisEntity LinkingKey Information ExtractionSemantic entity labelingTable Detectiondocument understanding

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Entity Linking FUNSD Doc2Graph F1 53.36 #7 of 7 Archive leaderboard report
Semantic entity labeling FUNSD Doc2Graph F1 82.25 #15 of 15 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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