• DocumentCode
    1908393
  • Title

    Multiresolution neural networks for omnifont character recognition

  • Author

    Wang, Jin ; Jean, Jack

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA
  • fYear
    1993
  • fDate
    1993
  • Firstpage
    1588
  • Abstract
    A multiresolution optical character recognition (OCR) using neural networks is proposed for omnifont character recognition. It is motivated by the human reading process in which a low resolution is used to effectively process the majority of clean and unambiguous text, while a more complicated recognition scheme is invoked only when a high resolution is needed. Compared with the method that utilizes single resolution, the multiresolution system not only speeds up recognition by up to 20 times, but also improves accuracy of isolated character recognition from 99.8% to 99.9%. The multiresolution approach captures the essence of better reading, and provides the building blocks for the next-generation OCR systems
  • Keywords
    neural nets; optical character recognition; OCR; multiresolution optical character recognition; neural networks; omnifont character recognition; Acoustic noise; Character recognition; Computer science; Humans; Lifting equipment; Neural networks; Optical character recognition software; Signal resolution; Switches; Text recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1993., IEEE International Conference on
  • Conference_Location
    San Francisco, CA
  • Print_ISBN
    0-7803-0999-5
  • Type

    conf

  • DOI
    10.1109/ICNN.1993.298793
  • Filename
    298793