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Author Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai
Title Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks Type Conference Article
Year 2022 Publication 17th European Conference on Computer Vision Workshops Abbreviated Journal
Volume 13804 Issue Pages (down) 329–344
Keywords
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.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-25068-2 Medium
Area Expedition Conference ECCV-TiE
Notes DAG; 600.162; 600.140; 110.312 Approved no
Call Number Admin @ si @ GBC2022 Serial 3795
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