![](bg8.png)
Johnson, J., Alahi, A., and Fei-Fei, L. (2016). Perceptual
losses for real-time style transfer and super-resolution.
In European conference on computer vision, pages
694–711. Springer.
Ledig, C., Theis, L., Husz
´
ar, F., Caballero, J., Cunningham,
A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang,
Z., et al. (2017). Photo-realistic single image super-
resolution using a generative adversarial network. In
Proceedings of the IEEE conference on computer vi-
sion and pattern recognition, pages 4681–4690.
Mao, X., Shen, C., and Yang, Y.-B. (2016). Image restora-
tion using very deep convolutional encoder-decoder
networks with symmetric skip connections. Advances
in neural information processing systems, 29.
Marnissi, M. A., Fradi, H., Sahbani, A., and Essoukri
Ben Amara, N. (2021). Thermal image enhancement
using generative adversarial network for pedestrian
detection. In 2020 25th International Conference on
Pattern Recognition (ICPR), pages 6509–6516. IEEE.
Moghaddam, R. F. and Cheriet, M. (2009). A variational
approach to degraded document enhancement. IEEE
transactions on pattern analysis and machine intelli-
gence, 32(8):1347–1361.
Niblack, W. (1985). An introduction to digital image pro-
cessing. Strandberg Publishing Company.
Otsu, N. (1979). A threshold selection method from gray-
level histograms. IEEE transactions on systems, man,
and cybernetics, 9(1):62–66.
Pantke, W., Dennhardt, M., Fecker, D., M
¨
argner, V., and
Fingscheidt, T. (2014). An historical handwritten
arabic dataset for segmentation-free word spotting-
hadara80p. In 2014 14th International Conference on
Frontiers in Handwriting Recognition, pages 15–20.
IEEE.
Pratikakis, I., Gatos, B., and Ntirogiannis, K. (2013). Ic-
dar 2013 document image binarization contest (dibco
2013). In 2013 12th International Conference on Doc-
ument Analysis and Recognition, pages 1471–1476.
IEEE.
Pratikakis, I., Zagoris, K., Barlas, G., and Gatos, B. (2017).
Icdar2017 competition on document image binariza-
tion (dibco 2017). In 2017 14th IAPR International
Conference on Document Analysis and Recognition
(ICDAR), volume 1, pages 1395–1403. IEEE.
Pratikakis, I., Zagoris, K., Kaddas, P., and Gatos, B.
(2018a). Icfhr 2018 competition on handwritten doc-
ument image binarization (h-dibco 2018). 2018 16th
International Conference on Frontiers in Handwriting
Recognition (ICFHR), pages 489–493.
Pratikakis, I., Zagoris, K., Kaddas, P., and Gatos, B.
(2018b). Icfhr2018 competition on handwritten doc-
ument image binarization contest (h-dibco 2018). In
International conference on frontiers in handwriting
recognition (ICFHR). IEEE, pages 1–1.
Sauvola, J. and Pietik
¨
ainen, M. (2000). Adaptive document
image binarization. Pattern recognition, 33(2):225–
236.
Souibgui, M. A. and Kessentini, Y. (2020). De-gan: A con-
ditional generative adversarial network for document
enhancement. IEEE Transactions on Pattern Analysis
and Machine Intelligence.
Su, B., Lu, S., and Tan, C. L. (2012). Robust docu-
ment image binarization technique for degraded docu-
ment images. IEEE transactions on image processing,
22(4):1408–1417.
Tamrin, M. O., El-Amine Ech-Cherif, M., and Cheriet,
M. (2021). A two-stage unsupervised deep learning
framework for degradation removal in ancient docu-
ments. In International Conference on Pattern Recog-
nition, pages 292–303. Springer.
Tensmeyer, C. and Martinez, T. (2017). Document image
binarization with fully convolutional neural networks.
In 2017 14th IAPR international conference on doc-
ument analysis and recognition (ICDAR), volume 1,
pages 99–104. IEEE.
Vo, Q. N., Kim, S. H., Yang, H. J., and Lee, G. (2018).
Binarization of degraded document images based on
hierarchical deep supervised network. Pattern Recog-
nition, 74:568–586.
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Tao, A., Kautz, J., and
Catanzaro, B. (2018). High-resolution image synthe-
sis and semantic manipulation with conditional gans.
In Proceedings of the IEEE conference on computer
vision and pattern recognition, pages 8798–8807.
Xiong, W., Jia, X., Xu, J., Xiong, Z., Liu, M., and Wang,
J. (2018). Historical document image binarization
using background estimation and energy minimiza-
tion. In 2018 24th International Conference on Pat-
tern Recognition (ICPR), pages 3716–3721. IEEE.
Yi, Z., Zhang, H., Tan, P., and Gong, M. (2017). Dual-
gan: Unsupervised dual learning for image-to-image
translation. In Proceedings of the IEEE international
conference on computer vision, pages 2849–2857.
Zamora-Mart
´
ınez, F., Espa
˜
na-Boquera, S., and Castro-
Bleda, M. (2007). Behaviour-based clustering of neu-
ral networks applied to document enhancement. In In-
ternational Work-Conference on Artificial Neural Net-
works, pages 144–151. Springer.
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A. (2017).
Unpaired image-to-image translation using cycle-
consistent adversarial networks. In Proceedings of
the IEEE international conference on computer vi-
sion, pages 2223–2232.
EHDI: Enhancement of Historical Document Images via Generative Adversarial Network
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