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@InProceedings{CSOC2018_HMKO,
  author    = {Michalak, Hubert and Okarma, Krzysztof},
  title     = {Fast Adaptive Image Binarization Using the Region Based Approach},
  booktitle = {Artificial Intelligence and Algorithms in Intelligent Systems},
  year      = {2019},
  editor    = {Silhavy, Radek},
  volume    = {764},
  series    = {Advances in Intelligent Systems and Computing},
  pages     = {79--90},
  doi       = {10.1007/978-3-319-91189-2_9},
  publisher = {Springer International Publishing},
  abstract  = {Adaptive binarization of unevenly lightened images is one of the key issues in document image analysis and further text recognition. As the global thresholding leads to improper results making correct text recognition practically impossible, an efficient implementation of adaptive thresholding is necessary. The most popular global approach is the use of Otsu's binarization which can be improved using the fast block based method or calculated locally leading to AdOtsu method. Even faster adaptive thresholding based on local mean calculated for blocks is presented in the paper. Obtained results have been compared with some other adaptive thresholding algorithms, being typically the modifications of Niblack's method, for a set of images originating from DIBCO databases modified by addition of intensity gradients. Obtained results confirm the usefulness of the proposed fast approach for binarization of document images.},
  isbn      = {978-3-319-91189-2},
}

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