Abstract
Thresholding of document images is one of the most relevant operations that influence the final results of their further analysis. Although many image binarization methods have been proposed during recent several years, starting from global thresholding, through local and adaptive methods, to more sophisticated multi-stage algorithms and the use of deep convolutional neural networks, proper thresholding of degraded historical document images is still an open challenge. Due to the release of the recent challenging DIBCO 2019 dataset, containing two categories of images with the particularly demanding papyri dataset (track B), most of the earlier proposed methods turned out to be significantly less effective for them. Analysing the competition’s results of the DIBCO 2019, apart from the variety of submitted trendy methods based on the use of deep learning, the great importance of image preprocessing may be observed for such demanding images. Hence, in this paper, we present the applicability analysis of the previously proposed preprocessing methods based on the use of the GGD and GMM in combination with various image binarization algorithms for this purpose. The obtained results are comparable with the state-of-the-art methods, including the top algorithms submitted to the recent DIBCO series contest.
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Michalak, H., Krupiński, R., Lech, P., Okarma, K. (2022). Preprocessing of Document Images Based on the GGD and GMM for Binarization of Degraded Ancient Papyri Images. In: Choraś, M., Choraś, R.S., Kurzyński, M., Trajdos, P., Pejaś, J., Hyla, T. (eds) Progress in Image Processing, Pattern Recognition and Communication Systems. CORES IP&C ACS 2021 2021 2021. Lecture Notes in Networks and Systems, vol 255. Springer, Cham. https://doi.org/10.1007/978-3-030-81523-3_11
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