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Hybrid Binarization Method for Historical Handwritten Documents

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Abstract

Binarization of historical documents is a rather complex task that is being intensively studied by researchers all over the world. A large number of approaches, procedures, and binarization algorithms have been proposed, but methods that work equally well in all cases have not yet been proposed. The literature offers various criteria for assessing the quality of the binarization result. In the case of binarization of ancient handwritten texts, the criterion for the quality of the binarization algorithm is the degree of readability of the text using a visual method or technical means. One of the approaches proposed in the literature to improve the quality of the binarization result is pre-processing the original image using filtering methods, morphological analysis, spectral analysis, etc. This article proposes a hybrid binarization method, consisting of an arbitrary global or adaptive binarization algorithm and a special segmentation procedure for selecting segments of certain sizes. The proposed procedure makes it possible to identify objects of certain sizes in an image, in particular artifacts that exist in a binarized image. This work experimentally explores the possibility of improving the quality of a binary image by applying the proposed procedure.

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REFERENCES

  1. Ambily, J., Jaini, S.B., Poorna, J., and Beena, K.V., Objective quality measures in binarization, Int. J. Comput. Sci. Inf. Technol., 2012, vol. 3, no. 4, pp. 4784–4788.

    Google Scholar 

  2. Ntirogiannis, K., Gatos, B., and Pratikakis, I., An objective evaluation methodology for document image binarization techniques, Proc. 8th IAPR Int. Workshop on Document Analysis Systems, Nara, 2008, pp. 217–224.

  3. Otsu, N., A threshold selection method from gray-level histograms, IEEE Trans. Syst., Man, Cybernet., 1979, vol. 9, no. 1, pp. 62–66.

    Article  Google Scholar 

  4. Sezgin, M. and Sankur, B., Survey over image thresholding techniques and quantitative performance evaluation, J. Electron. Imag., 2004, vol. 13, no. 1, pp. 146–165.

    Article  Google Scholar 

  5. Asatryan, D., Haroutunian, M., Sazhumyan, G., Kupriyanov, A., Paringer, R., and Kirsh, D., Comparative quality analysis of image global binarization procedures, Proc. 9th Int. Conf. on Information Technology and Nanotechnology (ITNT), Samara, 2023, pp. 1–5. https://doi.org/10.1109/ITNT57377.2023.10138953

  6. Niblack, W., An Introduction to Digital Image Processing, Englewood Cliffs, NJ: Prenlice Hall, 1956, pp. 115–116.

    Google Scholar 

  7. Sauvola, J. andPietikäinen, M., Adaptive document image binarization, Pattern Recogn., 2000, vol. 33, no. 2, pp. 225–236.

    Article  Google Scholar 

  8. He, J., Do, Q.D.M., Downton, A.C., and Kim, J.H., A comparison of binarization methods for historical archive documents, Proc. 8th Int. Conf. on Document Analysis and Recognition (ICDAR’05), Seoul, 2005, vol. 1, pp. 538–542.

  9. Ingle, P.D. and Kaur, P., Adaptive thresholding to robust image binarization for degraded document images, Proc. 1st Int. Conf. on Intelligent Systems and Information Management (ICISIM), Aurangabad, 2017, pp. 189–193.

  10. Gatos, B., Pratikakis, I., and Perantonis, S.J., Adaptive degraded document image binarization, Pattern Recogn., 2006, vol. 39, pp. 317–327.

    Article  Google Scholar 

  11. Cunzhao Shiy, Yanna Wangy, Baihua **aoy, and Chunheng Wang, OTSU guided adaptive binarization of CAPTCHA image using gamma correction, Proc. 23rd Int. Conf. on Pattern Recognition (ICPR), Cancun, 2016, pp. 3951–3956.

  12. Asatryan, D., Sazhumyan, G., and Sakanyan, B., Novel method for analysis of fingerprint poroscopical maps, Int. J. Inf. Content Process., 2014, vol. 1, no. 3, pp. 280–286.

    Google Scholar 

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ACKNOWLEDGMENTS

This work was supported by the Russian Foundation for Basic Research and RA Science Committee in the frames of the joint research project RFBR 20-51-05008 Аrm_a and SCS 20RF-144 accordingly.

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This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Correspondence to D. G. Asatryan, M. E. Haroutunian, G. S. Sazhumyan, A. V. Kupriyanov, R. A. Paringer or D. V. Kirsh.

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Asatryan, D.G., Haroutunian, M.E., Sazhumyan, G.S. et al. Hybrid Binarization Method for Historical Handwritten Documents. Program Comput Soft 49 (Suppl 1), S45–S50 (2023). https://doi.org/10.1134/S0361768823090037

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  • DOI: https://doi.org/10.1134/S0361768823090037

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