Advancements in Binarization and Noise Reduction Techniques for Ancient Script Preservation on Stone, Palm Leaves, and Copper Plates
Keywords:
Ancient Writings, Noise Reduction, Binarization Techniques, Stone Inscriptions, Palm Leaves, Copper Plates, Preservation, Enhancement.Abstract
This study specifically examines the methods used to protect and improve the condition of ancient texts that are engraved on three different types of materials: stone inscriptions, palm leaves, and copper plates. Every material poses distinct difficulties because of its texture, surface properties, and vulnerability to degradation over time. In order to minimize the negative impacts of noise, various noise reduction techniques such as median filtering, despeckle filtering, and Gaussian smoothing are utilized. Following that, well-established binarization methods such as Otsu, Niblack, and Sauvola are utilized to enhance the binarization process for different types of script materials. The effectiveness of these strategies is assessed using measures such as Mean Square Error (MSE), Structural Similarity Index Measure (SSIM), and Peak Signal-to-Noise Ratio (PSNR). The results indicate that the unique technique surpasses other methods in improving the quality of binarized text on all three types of script materials. This offers great opportunities for preserving and extracting vital textual information from these diverse and historically important mediums.
Downloads
References
N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," in IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, Jan. 1979, doi: 10.1109/TSMC.1979.4310076.
Niblack, Wayne. 1985 “An Introduction to Digital Image Processing”, Strandberg Publishing Company, DNK, 8787200554, 10.5555/4901.
Sauvola, J. and Pietaksinen, M. 2000. “Adaptive document image binarization”, Pattern Recognition, 33, pp. 225–336.
ARULPANDY, P.; PRICILLA, M. Trinita. Speckle Noise Reduction and Image Segmentation Based on a Modified Mean Filter. Computer Assisted Methods in Engineering and Science, [S.l.], v. 27, n. 4, p. 221–239, sep. 2020. ISSN 2956-5839.
B J Bipin Nair, KV Aadith Raj, M Kedar, S Pai Vaishak, EV Sreejil, Ancient Epic Manuscript Binarization and Classification Using False Color Spectralization and VGG-16 Model, Procedia Computer Science, Volume 218, 2023, Pages 631-643, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.01.045.
Bannigidad, Parashuram & Sajjan, S.. (2023). Restoration of Ancient Kannada Handwritten Palm Leaf Manuscripts Using Image Enhancement Techniques. 10.1007/978-3-031-28324-6_9.
Sudarsan, Dhanya & Sankar, Deepa. (2019). A Novel approach for Denoising palm leaf manuscripts using Image Gradient approximations. 506-511. 10.1109/ICECA.2019.8822224.
Rege, Priti. (2008). Enhancement of Palm Leaf Manuscript and Color Document Images with Synthetic Background Generation. Journal of Advances in Engineering Science. 25-34.
Singh, Mayank & Sreedevi, Indu. (2023). Denoising of palm leaf manuscripts using Gaussian filter and conservative smoothing. 050005. 10.1063/5.0142237.
Sachin Bhat, Seshikala G (2019). Restoration of Characters in Degraded Inscriptions using Phase Based Binarization and Geodesic Morphology . International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7, Issue-6, March 2019.
Sukanthi, S. Sakthivel Murugan and S. Hanis Binarization of Stone Inscription Images by Modified Bi-level Entropy Thresholding. Fluctuation and Noise Letters 2021 20:06.
Mustafa, Wan & Kader, Mohamed. (2018). Binarization of Document Images: A Comprehensive Review. Journal of Physics: Conference Series. 1019. 012023. 10.1088/1742-6596/1019/1/012023.
Omar Boudraa and Walid Khaled Hidouci and Dominique Michelucci (2019). Degraded Historical Documents Images Binarization Using a Combination of Enhanced Techniques. Computer Vision and Pattern Recognition.
Rasmana, S. T., Suprapto, Y. K., & Purnama, I. K. E. (2016). The new otsu thresholding for binarization of the ancient copper inscriptions. International Review on Computers and Software, 11(10), 907-914. https://doi.org/10.15866/irecos.v11i10.10359.
S. Das, S. Mandal and A. K. Das, "Binarization of stone inscripted documents," 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS), Bhubaneswar, India, 2015, pp. 11-16, doi: 10.1109/CGVIS.2015.7449883.
Monisha Munivel, V.S. Felix Enigo, MLIBT: A multi-level improvised binarization technique for Tamizhi inscriptions, Expert Systems with Applications, Volume 236, 2024, 121320, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.121320.
S. RajaKumar and V. Subbiah Bharathi, "Eighth century tamil consonants recognition from stone inscriptions," 2012 International Conference on Recent Trends in Information Technology, Chennai, India, 2012, pp. 40-43, doi: 10.1109/ICRTIT.2012.6206766.
Bhuvaneswari, G., and V. Subbiah Bharathi. “An Efficient Method for Digital Imaging of Ancient Stone Inscriptions.” Current Science, vol. 110, no. 2, 2016, pp. 245–50. JSTOR, http://www.jstor.org/stable/24906752. Accessed 2 Nov. 2023.
B. N. B J and N. S. Rani, "An Optimal Thresholder for Ancient Degraded Palm leaf Document Binarization," 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2021, pp. 1737-1744, doi: 10.1109/ICECA52323.2021.9676095.
S. Cherala and P. P. Rege, "Palm Leaf Manuscript/Color Document image Enhancement by Using Improved Adaptive Binarization Method," 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, India, 2008, pp. 687-692, doi: 10.1109/ICVGIP.2008.64.
M. W. A. Kesiman, S. Prum, J. -C. Burie and J. -M. Ogier, "An initial study on the construction of ground truth binarized images of ancient palm leaf manuscripts," 2015 13th International Conference on Document Analysis and Recognition (ICDAR), Tunis, Tunisia, 2015, pp. 656-660, doi: 10.1109/ICDAR.2015.7333843.
R. Chamchong and C. C. Fung, "Optimal selection of binarization techniques for the processing of ancient palm leaf manuscripts," 2010 IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey, 2010, pp. 3796-3800, doi: 10.1109/ICSMC.2010.5642008.
Sabeenian R S, Paramasivam M E and Dinesh P M, "Appraisal of localized binarization methods on Tamil palm-leaf manuscripts," 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India, 2016, pp. 793-797, doi: 10.1109/WiSPNET.2016.7566242.
K. Subramani and S. Murugavalli, "A novel binarization method for degraded tamil palm leaf images," 2016 Eighth International Conference on Advanced Computing (ICoAC), Chennai, India, 2017, pp. 176-181, doi: 10.1109/ICoAC.2017.7951765.
Huang, J. & Yang, X. Fast reduction of speckle noise in real ultrasound images. Signal Processing 93, 684–694 (2013).
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
