• DocumentCode
    1994131
  • Title

    Features for neural net based region identification of newspaper documents

  • Author

    Andersen, Tim ; Zhang, Wei

  • Author_Institution
    Comput. Sci. Dept., Boise State Univ., ID, USA
  • fYear
    2003
  • fDate
    3-6 Aug. 2003
  • Firstpage
    403
  • Abstract
    Several features for neural network based document region identification are tested. Specifically, this paper examines features for non-text region identification. The neural network based region identification algorithm is a key component of a document recognition system that segments a document into regions, classifies them into text, graphic, photo, and other region types, and then uses this classification to guide the processing and analysis of the image. The input data are unusually challenging: low quality images of newspaper documents obtained from microfilmed archives. The results compare favorably with other results reported in the literature.
  • Keywords
    document image processing; image recognition; image segmentation; information dissemination; neural nets; document classification; document recognition system; document segmentation; image analysis; image processing; image quality; microfilmed archives; neural net based region identification; neural network; newspaper documents; nontext region identification; region identification algorithm; Graphics; Image analysis; Image recognition; Image segmentation; Neural networks; Optical character recognition software; Pixel; Testing; Text analysis; Text recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Document Analysis and Recognition, 2003. Proceedings. Seventh International Conference on
  • Print_ISBN
    0-7695-1960-1
  • Type

    conf

  • DOI
    10.1109/ICDAR.2003.1227698
  • Filename
    1227698