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

23 Aug 2022  ·  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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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Entity Linking FUNSD Doc2Graph F1 53.36 # 3
Semantic entity labeling FUNSD Doc2Graph F1 82.25 # 7

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