Active Handwritten Character Recognition using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{teredesai:2001:EuroGP,
  author =       "Ankur Teredesai and J. Park and 
                 Venugopal Govindaraju",
  title =        "Active Handwritten Character Recognition using Genetic
                 Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and 
                 Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and 
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "371--379",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Pattern
                 Recognition, Active Character Recognition, Digit
                 Recognition, Handwritten digit classification: Poster",
  ISBN =         "3-540-41899-7",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=371",
  DOI =          "doi:10.1007/3-540-45355-5_30",
  size =         "10 pages",
  abstract =     "This paper is intended to demonstrate the effective
                 use of genetic programming in handwritten character
                 recognition. When the resources used by the classifier
                 increase incrementally and depend on the complexity of
                 classification task, we term such a classifier as
                 active. The design and implementation of active
                 classifiers based on genetic programming principles
                 becomes very simple and efficient. Genetic Programming
                 has helped optimize handwritten character recognition
                 problem in terms of feature set selection. We propose
                 an implementation with dynamism in pre-processing and
                 classification of handwritten digit images. This
                 paradigm will supplement existing methods by providing
                 better performance in terms of accuracy and processing
                 time per image for classification. Different levels of
                 informative detail can be present in image data and our
                 proposed paradigm helps highlight these information
                 rich zones. We compare our performance with passive and
                 active handwritten digit classification schemes that
                 are based on other pattern recognition techniques.",
  notes =        "EuroGP'2001, part of \cite{miller:2001:gp}",
}

Genetic Programming entries for Ankur M Teredesai Jaehwa Park Venugopal Govindaraju

Citations