Journal
2017 14TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR 2017), VOL 2
Volume -, Issue -, Pages 29-30Publisher
IEEE
DOI: 10.1109/ICDAR.2017.262
Keywords
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Funding
- European Union's research and innovation program under the Marie Sklodowska-Curie grant [665919]
- Spanish project [TIN2015-70924-C2-2-R]
- FPU from the Spanish Ministerio de Educacion, Cultura y Deporte [FPU15/06264]
- Ramon y Cajal Fellowship [RYC-2014-16831]
- CERCA Program/Generalitat de Catalunya
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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 and we show how they can be used in graphics recognition problems.
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