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

• URL: http://arxiv.org/abs/2208.11168v1
• Date: Tue, 23 Aug 2022 19:48:10 GMT
• Title: Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks
• Authors: Andrea Gemelli and Sanket Biswas and Enrico Civitelli and Josep Llad\'os and Simone Marinai
• Abstract summary: We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model. We evaluate our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection.
• Score: 0.965964228590342
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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