%0 Conference Proceedings
%T Graph-based deep learning for graphics classification
%A Pau Riba
%A Anjan Dutta
%A Josep Llados
%A Alicia Fornes
%B 14th International Conference on Document Analysis and Recognition
%D 2017
%F Pau Riba2017
%O DAG; 600.097; 601.302; 600.121
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3058), last updated on Fri, 06 Sep 2024 12:52:31 +0200
%X Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep  learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations andwe show how they can be used in graphics recognition problems
%U http://refbase.cvc.uab.es/files/RDL2017b.pdf
%U http://dx.doi.org/10.1109/ICDAR.2017.262
%P 29-30