Genetic Programming for Document Segmentation and Region Classification Using Discipulus

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@Article{Priyadharshini:2013:IJARAI,
  author =       "N. Priyadharshini and M. S. Vijaya",
  title =        "Genetic Programming for Document Segmentation and
                 Region Classification Using Discipulus",
  journal =      "International Journal of Advanced Research in
                 Artificial Intelligence",
  year =         "2013",
  volume =       "2",
  number =       "2",
  pages =        "15--22",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, OCR, Computer
                 Vision and Pattern Recognition",
  ISSN =         "2165-4050",
  URL =          "http://thesai.org/Publications/ViewIssue?volume=2&issue=2&code=IJARAI",
  URL =          "http://thesai.org/Downloads/IJARAI/Volume2No2/Paper_3-Genetic_programming_for_Document_Segmentation_And_Region_Classification_Using_Discipulus.pdf",
  URL =          "http://arxiv.org/abs/1303.0460",
  size =         "8 pages",
  abstract =     "Document segmentation is a method of rending the
                 document into distinct regions. A document is an
                 assortment of information and a standard mode of
                 conveying information to others. Pursuance of data from
                 documents involves ton of human effort, time intense
                 and might severely prohibit the usage of data systems.
                 So, automatic information pursuance from the document
                 has become a big issue. It is been shown that document
                 segmentation will facilitate to beat such problems.
                 This paper proposes a new approach to segment and
                 classify the document regions as text, image, drawings
                 and table. Document image is divided into blocks using
                 Run length smearing rule and features are extracted
                 from every blocks. Discipulus tool has been used to
                 construct the Genetic programming based classifier
                 model and located 97.5percent classification
                 accuracy.",
  notes =        "See also \cite{oai:arXiv.org:1303.0460}
                 http://thesai.org/Publications/IJARAI",
}

Genetic Programming entries for N Priyadharshini M S Vijaya

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