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)

Abstract

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.

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Copyright information

© 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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