Behaviour-Based Clustering of Neural Networks Applied to Document Enhancement

  • F. Zamora-Martínez
  • S. España-Boquera
  • M. J. Castro-Bleda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)


This work proposes an agglomerative hierarchical clustering algorithm where the items to be clustered are supervised-learning classifiers. The measure of similarity to compare classifiers is based on their behaviour. This clustering algorithm has been applied to document enhancement: A set of neural filters is trained with multilayer perceptrons for different types of noise and then clustered into groups to obtain a reduced set of neural clustered filters. In order to automatically determine which clustered filter is the most suitable to clean and enhance a real noisy image, an image classifier is also trained using multilayer perceptrons.


Multilayer Perceptrons Document Image Noisy Image Optical Character Recognition Agglomerative Hierarchical 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • F. Zamora-Martínez
    • 1
  • S. España-Boquera
    • 2
  • M. J. Castro-Bleda
    • 2
  1. 1.Departamento de Lenguajes y Sistemas Informáticos, Universitat Jaume I, CastellónSpain
  2. 2.Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, ValenciaSpain

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