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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2012: Artificial Intelligence and Soft Computing pp 543–550Cite as

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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  • DOI: 10.1007/978-3-642-29347-4_63
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Author information

Authors and Affiliations

  1. Institut für Informatik und Mathematik, Free University of Berlin, Takustr. 9, 14195, Berlin, Germany

    Marcus Lindner, Marco Block & Raúl Rojas

Authors
  1. Marcus Lindner
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  2. Marco Block
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  3. Raúl Rojas
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski, Marcin Korytkowski & Rafał Scherer,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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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

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