• DocumentCode
    1798602
  • Title

    Binarization of degraded document image using Gaussian Markov random field model

  • Author

    Shujing Lu ; Yue Lu

  • Author_Institution
    Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China
  • fYear
    2014
  • fDate
    7-9 July 2014
  • Firstpage
    272
  • Lastpage
    276
  • Abstract
    This paper presents a binarization approach to degraded document images, which is based on Gaussian Markov Random Field (GMRF) model. The energy function with the single-site and pair-site clique potential functions is formulated for the GMRF. The parameters of the potential functions are estimated by expectation-maximization (EM) algorithm, without necessity of training process. Experiments on different types of degraded document images with various noise, contrast variation or uneven illumination, have demonstrated the validity of the proposed method.
  • Keywords
    Gaussian processes; Markov processes; document image processing; expectation-maximisation algorithm; random processes; GMRF; Gaussian Markov random field model; contrast variation; degraded document image binarization approach; energy function; expectation-maximization algorithm; pair-site clique potential functions; single-site clique potential functions; uneven illumination; Analytical models; Computational modeling; Convergence; Markov random fields; Mathematical model; Pattern recognition; Probability density function; Binarization; Gaussian Markov Random Field; expectation-maximization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Audio, Language and Image Processing (ICALIP), 2014 International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4799-3902-2
  • Type

    conf

  • DOI
    10.1109/ICALIP.2014.7009799
  • Filename
    7009799