Metode Perbaikan Citra Naskah Kuno Menggunakan Histogram Terekualisasi Lokal dan Global
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Abstract
Ancient manuscripts are precious relics of the past. These manuscripts are very susceptible to disturbances, including changes in the colour of the paper. For example, the paper may become dark and black (known as foxing), and there may be black spots due to exposure to water. These changes in paper colour can affect the comfort and legibility of the document. In order to preserve and protect these ancient manuscripts, any manuscript that shows signs of damage must be repaired. One effort to restore scanned ancient manuscripts involves the use of local and global image restoration methods based on histogram equalisation. In this research project, histogram equalisation is used to improve image quality by eliminating foxing and improving the contrast between text and background. Based on the Mean Opinion Score (MOS) values obtained from the tests, the histogram equalisation method is effective in eliminating foxing compared to the Otsu method.
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References
I. Pratikakis, K. Zagoris, G. Barlas, and B. Gatos, “ICFHR 2016 handwritten document image binarization contest (H-DIBCO 2016),” Proc. Int. Conf. Front. Handwrit. Recognition, ICFHR, vol. 0, pp. 619–623, 2016, doi: 10.1109/ICFHR.2016.0118.
B. Su, S. Lu, and C. L. Tan, “Robust document image binarization technique for degraded document images,” IEEE Trans. Image Process., vol. 22, no. 4, pp. 1408–1417, 2013, doi: 10.1109/TIP.2012.2231089.
H. Lombok et al., “Denoising Dan Binarization Untuk Pengolahan Citra,” pp. 28–29, 2016.
A. T. Gallop, “a Jawi Sourcebook for the Study of Malay Palaeography and Orthography,” Indones. Malay World, vol. 43, no. 125, pp. 13–171, 2015, doi: 10.1080/13639811.2015.1008253.
A. Tonazzini, P. Savino, E. Salerno, M. Hanif, and F. Debole, “Virtual restoration and content analysis of ancient degraded manuscripts,” iJIST Int. J. Inf. Sci. Technol., vol. 3, no. 5, pp. 16–25, 2019.
O. Sihombing, E. Buulolo, H. K. Siburian, G. Batak, and M. O. Morfologis, “Hasil Segmentasi Citra Digital Gorga Batak,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 2, pp. 40–48, 2018.
A. Baldominos, Y. Saez, and P. Isasi, “A survey of handwritten character recognition with MNIST and EMNIST,” Appl. Sci., vol. 9, no. 15, 2019, doi: 10.3390/app9153169.
S. N. H. S. Abdullah, S. M. Ismail, M. K. Hasan, and P. Shivakumara, “Novel Adaptive Binarization Method for Degraded Document Images,” Comput. Mater. Contin., vol. 67, no. 3, pp. 3815–3832, 2021, doi: 10.32604/cmc.2021.014610.
G. Lazzara and T. Géraud, “Efficient multiscale Sauvola’s binarization,” Int. J. Doc. Anal. Recognit., vol. 17, no. 2, pp. 105–123, 2014, doi: 10.1007/s10032-013-0209-0.
S. U. Khan, I. Ullah, F. Khan, Y. Lee, and S. Ullah, “Historical Text Image Enhancement Using Image Scaling and Generative Adversarial Networks,” Sensors, vol. 23, no. 8, 2023, doi: 10.3390/s23084003.
F. Jia, C. Shi, K. He, C. Wang, and B. Xiao, “Degraded document image binarization using structural symmetry of strokes,” Pattern Recognit., vol. 74, pp. 225–240, 2018, doi: 10.1016/j.patcog.2017.09.032.
U. Rani, A. Kaur, and G. Josan, “A new binarization method for degraded document images,” Int. J. Inf. Technol., vol. 15, no. 2, pp. 1035–1053, 2023, doi: 10.1007/s41870-019-00361-3.
W. Boussellaa, A. Zahour, H. Elabed, A. Benabdelhafid, and A. Alimi, “Unsupervised block covering analysis for text-line segmentation of Arabic ancient handwritten document images,” Proc. - Int. Conf. Pattern Recognit., no. August, pp. 1929–1932, 2010, doi: 10.1109/ICPR.2010.475.
Y. Chang, “Improving the Otsu method for MRA image vessel extraction via resampling and ensemble learning,” Healthc. Technol. Lett., vol. 6, no. 4, pp. 115–120, 2019, doi: 10.1049/htl.2018.5031.
O. Patel, Y. P. S. Maravi, and S. Sharma, “A Comparative Study of Histogram Equalization Based Image Enhancement Techniques for Brightness Preservation and Contrast Enhancement,” Signal Image Process. An Int. J., vol. 4, no. 5, pp. 11–25, 2013, doi: 10.5121/sipij.2013.4502.