Finding Arbitrary Shaped Clusters for Character Recognition View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2008

AUTHORS

Noha A. Yousri , Mohamed. S. Kamel , Mohamed. A. Ismail

ABSTRACT

Several supervised and unsupervised methods have been applied to the field of character recognition. In this research we focus on the unsupervised methods used to group similar characters together. Instead of using the traditional clustering algorithms, which are mainly restricted to globular-shaped clusters, we use an efficient distance based clustering that identifies the natural shapes of clusters according to their densities. Thus, in the case of character recognition, where it is natural to have different writing styles for the same character, the algorithm can be used to discover the continuity between character feature vectors, which cannot be discovered by traditional algorithms. This paper |introduces the use of an algorithm that efficiently finds arbitrary-shaped clusters of characters, and compares it to related algorithms. Two character recognition data sets are used to illustrate the efficiency of the suggested algorithm. More... »

PAGES

597-608

References to SciGraph publications

  • 1997-06. BIRCH: A New Data Clustering Algorithm and Its Applications in DATA MINING AND KNOWLEDGE DISCOVERY
  • 1985-12. Comparing partitions in JOURNAL OF CLASSIFICATION
  • Book

    TITLE

    Image Analysis and Recognition

    ISBN

    978-3-540-69811-1
    978-3-540-69812-8

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-540-69812-8_59

    DOI

    http://dx.doi.org/10.1007/978-3-540-69812-8_59

    DIMENSIONS

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