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Author (up) Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal edit   pdf
url  doi
  Title Graph-Based Deep Generative Modelling for Document Layout Generation Type Conference Article
  Year 2021 Publication 16th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume 12917 Issue Pages 525-537  
  Abstract 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.  
  Address Lausanne; Suissa; September 2021  
  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 Medium  
  Area Expedition Conference  
  Notes DAG; 600.121; 600.140; 110.312 Approved no  
  Call Number Admin @ si @ BRL2021 Serial 3676  
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