%0 Conference Proceedings
%T Graph-Based Deep Generative Modelling for Document Layout Generation
%A Sanket Biswas
%A Pau Riba
%A Josep Llados
%A Umapada Pal
%B 16th International Conference on Document Analysis and Recognition
%D 2021
%V 12917
%F Sanket Biswas2021
%O DAG; 600.121; 600.140; 110.312
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3676), last updated on Tue, 07 Nov 2023 13:35:13 +0100
%X One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices.
%U https://link.springer.com/chapter/10.1007/978-3-030-86159-9_38
%U http://refbase.cvc.uab.es/files/BRL2021.pdf
%U http://dx.doi.org/10.1007/978-3-030-86159-9_38
%P 525-537