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Author (up) Ernest Valveny; Enric Marti edit   pdf
doi  openurl
  Title Learning of structural descriptions of graphic symbols using deformable template matching Type Conference Article
  Year 2001 Publication Proc. Sixth Int Document Analysis and Recognition Conf Abbreviated Journal  
  Volume Issue Pages 455-459  
  Abstract Accurate symbol recognition in graphic documents needs an accurate representation of the symbols to be recognized. If structural approaches are used for recognition, symbols have to be described in terms of their shape, using structural relationships among extracted features. Unlike statistical pattern recognition, in structural methods, symbols are usually manually defined from expertise knowledge, and not automatically infered from sample images. In this work we explain one approach to learn from examples a representative structural description of a symbol, thus providing better information about shape variability. The description of a symbol is based on a probabilistic model. It consists of a set of lines described by the mean and the variance of line parameters, respectively providing information about the model of the symbol, and its shape variability. The representation of each image in the sample set as a set of lines is achieved using deformable template matching.  
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  Language Summary Language Original Title  
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  Area Expedition Conference  
  Notes DAG;IAM; Approved no  
  Call Number IAM @ iam @ VMA2001 Serial 1654  
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