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Object Recognition Using Summed Features Classifier

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Part of the Lecture Notes in Computer Science book series (LNAI,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.

Keywords

  • 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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Lindner, M., Block, M., Rojas, R. (2012). Object Recognition Using Summed Features Classifier. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_63

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  • DOI: https://doi.org/10.1007/978-3-642-29347-4_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29346-7

  • Online ISBN: 978-3-642-29347-4

  • eBook Packages: Computer ScienceComputer Science (R0)