• DocumentCode
    396687
  • Title

    Using artificial neural networks to identify headings in newspaper documents

  • Author

    Zhang, Wei ; Andersen, Timothy L.

  • Author_Institution
    Comput. Sci. Dept., Boise State Univ., ID, USA
  • Volume
    3
  • fYear
    2003
  • fDate
    20-24 July 2003
  • Firstpage
    2283
  • Abstract
    Several features for Neural Network based document region identification are tested. Specifically, this paper examines features for headline and subheadline 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. Experiments on several newspaper documents show that the features used are capable of robust and accurate headline identification.
  • Keywords
    document image processing; learning (artificial intelligence); neural nets; pattern classification; artificial neural networks; document recognition system; headline region identification; image analysis; image processing; microfilm archives; newspaper documents; newspaper headings identification; subheadline region identification; Artificial neural networks; Graphics; Image analysis; Image recognition; Image segmentation; Intelligent networks; Optical character recognition software; Pixel; Text analysis; Text recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2003. Proceedings of the International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7898-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2003.1223767
  • Filename
    1223767