Object Recognition Using Summed Features Classifier

  • Marcus Lindner
  • Marco Block
  • Raúl Rojas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7267)


A common task in the field of document digitization for information retrieval is separating text and non-text elements. In this paper an innovative approach of recognizing patterns is presented. Statistical and structural features in arbitrary number are combined into a rating tree, which is an adapted decision tree. Such a tree is trained for character patterns to distinguish text elements from non-text elements. First experiments in a binarization application have shown promising results in significant reduction of false-positives without producing false-negatives.


Feature Vector Object Recognition Child Node Markov Chain Monte Carlo Method Agglomerative Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marcus Lindner
    • 1
  • Marco Block
    • 1
  • Raúl Rojas
    • 1
  1. 1.Institut für Informatik und MathematikFree University of BerlinBerlinGermany

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