Learning and Upgrading Rules for an Optical Character Recognition System Using Genetic Programming

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

@InCollection{andre:1997:HEC,
  author =       "David Andre",
  title =        "Learning and Upgrading Rules for an Optical Character
                 Recognition System Using Genetic Programming",
  booktitle =    "Handbook of Evolutionary Computation",
  publisher =    "Oxford University Press",
  publisher_2 =  "Institute of Physics Publishing",
  year =         "1997",
  editor =       "Thomas Baeck and David B. Fogel and 
                 Zbigniew Michalewicz",
  chapter =      "section G8.1",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7503-0392-1",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
  DOI =          "doi:10.1201/9781420050387.ptg",
  size =         "8 pages",
  abstract =     "Rule-based systems used for optical character
                 recognition (OCR) are notoriously difficult to write,
                 maintain, and upgrade. This case study describes a
                 method for using genetic programming (GP) to
                 automatically generate and upgrade rules for an OCR
                 system. Sets of rules for recognizing a single
                 character are encoded as LISP programs and are evolved
                 using GP. The rule sets are programs that evolve to
                 examine a set of preprocessed features using complex
                 constructs including iteration, pointers, and memory.
                 The system was successful at learning rules for large
                 character sets consisting of multiple fonts and sizes,
                 with good generalization to test sets. In addition, the
                 method was found to be successful at updating
                 human-coded rules written in C for new fonts. This
                 research demonstrates the successful application of GP
                 to a difficult, noisy, real-world problem, and
                 introduces GP as a method for learning sets of rules.",
  notes =        "invited chapter",
}

Genetic Programming entries for David Andre

Citations