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@InProceedings{ICCS2019,
  author    = {Krupi{\'{n}}ski, Robert and Lech, Piotr and Tec{\l}aw, Mateusz and Okarma, Krzysztof},
  title     = {Binarization of Degraded Document Images with Generalized Gaussian Distribution},
  booktitle = {Computational Science -- ICCS 2019},
  year      = {2019},
  editor    = {Rodrigues, Jo{\~a}o M. F. and Cardoso, Pedro J. S. and Monteiro, J{\^a}nio and Lam, Roberto and Krzhizhanovskaya, Valeria V. and Lees, Michael H. and Dongarra, Jack J. and Sloot, Peter M.A.},
  volume    = {11540},
  series    = {Lecture Notes in Computer Science},
  pages     = {177--190},
  address   = {Cham},
  publisher = {Springer International Publishing},
  abstract  = {One of the most crucial steps of preprocessing of document images subjected to further text recognition is their binarization, which influences significantly obtained OCR results. Since for degrades images, particularly historical documents, classical global and local thresholding methods may be inappropriate, a challenging task of their binarization is still up-to-date. In the paper a novel approach to the use of Generalized Gaussian Distribution for this purpose is presented. Assuming the presence of distortions, which may be modelled using the Gaussian noise distribution, in historical document images, a significant similarity of their histograms to those obtained for binary images corrupted by Gaussian noise may be observed. Therefore, extracting the parameters of Generalized Gaussian Distribution, distortions may be modelled and removed, enhancing the quality of input data for further thresholding and text recognition. Due to relatively long processing time, its shortening using the Monte Carlo method is proposed as well. The presented algorithm has been verified using well-known DIBCO datasets leading to very promising binarization results.},
  doi       = {10.1007/978-3-030-22750-0_14},
  isbn      = {978-3-030-22750-0},
}

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