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Adaptive document image binarization

Sauvola, J., Pietikäinen, M.·2000·Pattern Recognition

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Research works printed but not linked: 23

  1. J. Sauvola, M. Pietikäinen, Page segmentation and classification using fast feature extraction and connectivity analysis, International Conference on Document Analysis and Recognition, ICDAR '95, Montreal, Canada, 1995, pp. 1127–1131. · DOI
  2. H. Baird, Document image defect models, Proceedings of the IAPR Workshop on Syntactic and Structural Pattern Recognition, 1990, pp. 38–46.
  3. O'Gorman, 1994, Binarization and multithresholding of document images using connectivity, CVGIP, 56, 496
  4. Y. Liu, R. Fenrich, S.N. Srihari, An object attribute thresholding algorithm for document image binarization, International Conference on Document Analysis and Recognition, ICDAR '93, Japan, 1993, pp. 278–281. · DOI
  5. Yang, 1994, Adaptive thresholding algorithm and its hardware implementation, Pattern Recognition Lett., 15, 141, 10.1016/0167-8655(94)90043-4 · DOI
  6. M. Chang, S. Kang, W. Rho, H. Kim, D, Kim, Improved binarization algorithm for document image by histogram and edge detection, International Conference for Document Analysis and Recognition ICDAR '95, Montreal, Canada, 1995, pp. 636–643.
  7. T. Pavlidis, Threshold selection using second derivatives of the gray scale image, International Conference on Document Analysis and Recognition, ICDAR '93, Japan, 1993, pp. 274–277. · DOI
  8. Rosenfeld, 1981, Thresholding using relaxation, IEEE Trans. Pattern Anal. Mach. Intell., PAMI-3, 598, 10.1109/TPAMI.1981.4767152 · DOI
  9. Perez, 1987, An iterative thresholding algorithm for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., PAMI-9, 742, 10.1109/TPAMI.1987.4767981 · DOI
  10. J.R. Parker, C. Jennings, A.G. Salkauskas, Thresholding using an illumination model, ICDAR '93, Japan, 1993, pp. 270–273. · DOI
  11. V.A. Shapiro, P.K. Veleva, V.S. Sgurev, An adaptive method for image thresholding, Proceedings of the 11th KPR, 1992, pp. 696–699. · DOI
  12. Pikaz, 1996, Digital image thresholding, based on topological stable-state, Pattern Recognition, 29, 829, 10.1016/0031-3203(95)00126-3 · DOI
  13. Henstock, 1996, Automatic gradient threshold determination for edge detection, IEEE Trans. Image Processing, 5, 784, 10.1109/83.499917 · DOI
  14. Chen, 1994, A fast two-dimensional entropic thresholding algorithm, Pattern Recognition, 27, 885, 10.1016/0031-3203(94)90154-6 · DOI
  15. Yanowitz, 1989, A new method for image segmentation, CVGIP, 46, 82
  16. S. Nieminen, J. Sauvola, T. Seppänen, M. Pietikäinen, A benchmarking system for document analysis algorithms, Proc. SPIE 3305 Document Recognition V 3305 (1998) 100–111.
  17. S.T. Welstead, Neural Network and Fuzzy Logic Applications in C/C++, Wiley, New York, 1994, p. 494.
  18. W. Niblack, An Introduction to Image Processing, Prentice-Hall, Englewood Cliffs, NJ, 1986, pp. 115–116.
  19. Trier, 1995, Goal-directed evaluation of binarization methods, IEEE Trans. Pattern Anal. Mach. Intell., 17, 1191, 10.1109/34.476511 · DOI
  20. L. Eikvil, T. Taxt, K. Moen, A fast adaptive method for binarization of document images, International Conference on Document Analysis and Recognition, ICDAR '91, France, 1991, pp. 435–443.
  21. J. Bernsen, Dynamic thresholding of grey-level images, Proceedings of the Eighth ICPR, 1986, pp. 1251–1255.
  22. Parker, 1991, Gray level thresholding on badly illuminated images, IEEE Trans. Pattern Anal. Mach. Intell., 13, 813, 10.1109/34.85672 · DOI
  23. J. Sauvola, S. Haapakoski, H. Kauniskangas, T. Seppänen, M. Pietikäinen, D. Doermann, A distributed management system for testing document image analysis algorithms, 4th ICDAR, Germany, 1997, pp. 989–995. · DOI

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Metadata

Authors
Sauvola, J., & Pietikäinen, M.
Venue
Pattern Recognition
Publisher
Elsevier BV
Published
2000-02
Volume
33
Issue
2
Pages
225-236
ISSN
0031-3203 (ISSN-L)
OpenAlex
W2128060444
Cited by
2,339
References
0
FWCI
5.26
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.