Genetic Engineering of Hierarchical Fuzzy Regional Representations for Handwritten Character Recognition

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@Article{Gagne:2006:ijDAR,
  author =       "Christian Gagne and Marc Parizeau",
  title =        "Genetic Engineering of Hierarchical Fuzzy Regional
                 Representations for Handwritten Character Recognition",
  journal =      "International Journal on Document Analysis and
                 Recognition",
  year =         "2006",
  volume =       "8",
  number =       "4",
  pages =        "223--231",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://vision.gel.ulaval.ca/fr/publications/Id_607/PublDetails.php",
  keywords =     "genetic algorithms, genetic programming",
  doi =          "doi:10.1007/s10032-005-0005-6",
  abstract =     "This paper presents a genetic programming based
                 approach for optimising the feature extraction step of
                 a handwritten character recogniser. This recognizer
                 uses a simple multilayer perceptron as a classifier and
                 operates on a hierarchical feature space of
                 orientation, curvature, and centre of mass primitives.
                 The nodes of the hierarchy represent rectangular
                 sub-regions of their parent node, the tree root
                 corresponding to the character's bounding box. Within
                 each sub-region, a variable number of fuzzy features
                 are extracted. Genetic programming is used to
                 simultaneously learn the best hierarchy and the best
                 combination of fuzzy features. Moreover, the fuzzy
                 features are not predetermined, they are inferred from
                 the evolution process which runs a two-objective
                 selection operator. The first objective maximises the
                 recognition rate, and the second minimises the feature
                 space size. Results on Unipen data show that, using
                 this approach, robust representations could be obtained
                 that out-performed comparable human-designed
                 hierarchical fuzzy regional representations.",
}

Genetic Programming entries for Christian Gagne Marc Parizeau