Frequent Graph Discovery: Application to Line Drawing Document Images


  • Eugen Barbu
  • Pierre Heroux
  • Sebastien Adam
  • Eric Trupin


In this paper a sequence of steps is applied to a graph representation of line drawings using concepts from data mining. This process finds frequent subgraphs and then association rules between these subgraphs. The distant aim is the automatic discovery of symbols and their relations, which are parts of the document model. The main outcome of our work is firstly an algorithm that finds frequent subgraphs in a single graph setting and secondly a modality to find rules and meta-rules between the discovered subgraphs. The searched structures are closed [1] and disjunct subgraphs. One aim of this study is to use the discovered symbols for classification and indexation of document images when a supervised approach is not at hand. The relations found between symbols can be used in segmentation of noisy and occluded document images. The results show that this approach is suitable for patterns, symbols or relation discovery.


graph mining, symbol discovery




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