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IAPR Workshop on Artificial Neural Networks in Pattern Recognition

ANNPR 2012: Artificial Neural Networks in Pattern Recognition pp 104–114Cite as

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On Graph-Associated Matrices and Their Eigenvalues for Optical Character Recognition

On Graph-Associated Matrices and Their Eigenvalues for Optical Character Recognition

  • Miriam Schmidt,
  • Günther Palm &
  • Friedhelm Schwenker 
  • Conference paper
  • 1259 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7477)

Abstract

In this paper, the classification power of the eigenvalues of six graph-associated matrices is investigated and evaluated on a benchmark dataset for optical character recognition. The extracted eigenvalues were utilized as feature vectors for multi-class classification using support vector machines. Each graph-associated matrix contains a certain type of geometric/spacial information, which may be important for the classification process. Classification results are presented for all six feature types, as well as for classifier combinations at decision level. For the decision level combination probabilistic output support vector machines have been applied. The eigenvalues of the weighted adjacency matrix provided the best classification rate of 89.9 %. Here, almost half of the misclassified letters are confusion pairs, such as I-L and N-Z. This classification performance can be increased by decision fusion, using the sum rule, to 92.4 %.

Keywords

  • graph classification
  • weighted adjacency matrix
  • spectrum
  • support vector machine

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Authors
  1. Miriam Schmidt
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  2. Günther Palm
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  3. Friedhelm Schwenker
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Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler (FBK), 38123, Trento, Italy

    Nadia Mana

  2. Institute of Neural Information Processing, University of Ulm, 89069, Ulm, Germany

    Friedhelm Schwenker

  3. Dipartimento di Ingegneria dell’Informazione, Università di Siena, 53100, Siena, Italy

    Edmondo Trentin

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© 2012 Springer-Verlag Berlin Heidelberg

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Schmidt, M., Palm, G., Schwenker, F. (2012). On Graph-Associated Matrices and Their Eigenvalues for Optical Character Recognition. In: Mana, N., Schwenker, F., Trentin, E. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2012. Lecture Notes in Computer Science(), vol 7477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33212-8_10

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  • DOI: https://doi.org/10.1007/978-3-642-33212-8_10

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  • Print ISBN: 978-3-642-33211-1

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