MLIBT: A multi-level improvised binarization technique for Tamizhi inscriptions
[Display omitted] •To create standard Tamizhi dataset: capturing onsite inscriptions, collecting from various ASI, annotating the collected datasets by domain experts and validated by the subject experts.•To enlarge the custom dataset by data augmentation to test the suitability for deep learning ap...
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| Published in | Expert systems with applications Vol. 236; p. 121320 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.02.2024
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| Subjects | |
| Online Access | Get full text |
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| Abstract | [Display omitted]
•To create standard Tamizhi dataset: capturing onsite inscriptions, collecting from various ASI, annotating the collected datasets by domain experts and validated by the subject experts.•To enlarge the custom dataset by data augmentation to test the suitability for deep learning approach for Tamizhi inscription binaraization.•To develop a multi-level improvised binarization algorithm for the Tamizhi inscription to separate the foreground text from the stone background by applying improved median filtering, iterative thresholding and modified adaptive thresholding.•To perform post-processing optimization for the binarized results using swell and shrink filters.
The Tamizhi inscriptions, one of the earliest ever discovered, is predominantly found on memorial stones and caves which dates 5th century BCE to 3rd century CE. Today’s generations need ways to interpret the script in order to know the historical figures and events because the Tamizhi script evolved into the modern Tamil script over time. Currently, only few epigraphists are available to manually decode the inscriptions into modern Tamil. Hence, there is a need for an alternate way to preserve this cultural heritage. Image processing is one such digital technology that enables binarization on inscription images, and the retrieved text may then be utilized to convert them to the needed target language. Nevertheless, binarization of Tamizhi inscription images are highly complex due to aging, environmental factors, handwritten, similar foreground & background and uneven size and shapes of the stones. Also, due to the small dataset, deep learning techniques are inapplicable. Furthermore, existing approaches produce poor results for Tamizhi inscriptions since they can only be used on flat stone backgrounds and require sufficient light illumination for effective binarization. This research suggests a multi-level improvised binarization solution for Tamizhi inscription images to address these challenges. It achieves this utilizing post-processing with shrink and swell filters together with an improved median filter with modified adaptive thresholding. Outperforming the current binarization techniques, which achieved a maximum accuracy of about 74%, MLIBT produced an accuracy of around 92.19%. |
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| AbstractList | [Display omitted]
•To create standard Tamizhi dataset: capturing onsite inscriptions, collecting from various ASI, annotating the collected datasets by domain experts and validated by the subject experts.•To enlarge the custom dataset by data augmentation to test the suitability for deep learning approach for Tamizhi inscription binaraization.•To develop a multi-level improvised binarization algorithm for the Tamizhi inscription to separate the foreground text from the stone background by applying improved median filtering, iterative thresholding and modified adaptive thresholding.•To perform post-processing optimization for the binarized results using swell and shrink filters.
The Tamizhi inscriptions, one of the earliest ever discovered, is predominantly found on memorial stones and caves which dates 5th century BCE to 3rd century CE. Today’s generations need ways to interpret the script in order to know the historical figures and events because the Tamizhi script evolved into the modern Tamil script over time. Currently, only few epigraphists are available to manually decode the inscriptions into modern Tamil. Hence, there is a need for an alternate way to preserve this cultural heritage. Image processing is one such digital technology that enables binarization on inscription images, and the retrieved text may then be utilized to convert them to the needed target language. Nevertheless, binarization of Tamizhi inscription images are highly complex due to aging, environmental factors, handwritten, similar foreground & background and uneven size and shapes of the stones. Also, due to the small dataset, deep learning techniques are inapplicable. Furthermore, existing approaches produce poor results for Tamizhi inscriptions since they can only be used on flat stone backgrounds and require sufficient light illumination for effective binarization. This research suggests a multi-level improvised binarization solution for Tamizhi inscription images to address these challenges. It achieves this utilizing post-processing with shrink and swell filters together with an improved median filter with modified adaptive thresholding. Outperforming the current binarization techniques, which achieved a maximum accuracy of about 74%, MLIBT produced an accuracy of around 92.19%. |
| ArticleNumber | 121320 |
| Author | Munivel, Monisha Felix Enigo, V.S. |
| Author_xml | – sequence: 1 givenname: Monisha orcidid: 0000-0002-5671-5094 surname: Munivel fullname: Munivel, Monisha email: moni.munivel@outlook.com – sequence: 2 givenname: V.S. surname: Felix Enigo fullname: Felix Enigo, V.S. email: felixvs@ssn.edu.in |
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| Keywords | Modified adaptive thresholding Cave inscriptions Iterative thresholding Tamizhi inscription images Binarization Memorial stones Median filter |
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| References | 10.1109/CFIS54774.2022.9756490. Bataineh, Abdullah, Omar (b0010) 2017; 20 Muller, Alonso-Caneiro, Read, Vincent, Collins (b0110) 2022; 11 Sreedevi, Pandey, Jayanthi, Bhola, Chaudhury (b0160) 2013; 2013 Sukanthi, S. S., & Hanis, S. (b0165) 2021; 20 (10). 10.3390/jimaging8100272. 11–16. 10.1109/CGVIS.2015.7449883. Lins, R. D., Bernardino, R., Barboza, R. da S., & De Oliveira, R. C. (2022). Using Paper Texture for Choosing a Suitable Algorithm for Scanned Document Image Binarization. Gatos, Pratikakis, Perantonis (b0050) 2006; 39 Xu, W., Souly, N., & Brahma, P. P. (2021). Reliability of GAN Generated Data to Train and Validate Perception Systems for Autonomous Vehicles. J. Bernsen. (1986). Dynamic Thresholding of Gray Level Image. In Saccenti, Hendriks, Smilde (b0145) 2020; 10 Rasheed, Ali, Alabdali, Shihab, Rashid, Rashid, Abed Hamad (b0135) 2021; 1999 Chicco, Warrens, Jurman (b0025) 2021; 7 Zhu, Wang (b0195) 2012; 7 171–180. 10.1109/WACVW52041.2021.00023. Das, S., Mandal, S., & Das, A. K. (2016). Binarization of stone inscripted documents. Ramaneswaran, Srinivasan, Vincent, Chang (b0130) 2021; 2021 , Wang, Wang, Kwan Ng, Schober, Gao (b0175) 2021; 20 Bera, Ghosh, Bhowmik, Sarkar, Nasipuri (b0015) 2021; 80 . Tran, Tran, Nguyen, Nguyen, Cheung (b0170) 2021; 30 (Issue January). 10.5772/9776. 2488–2495. 10.1109/CVPR.2010.5539950. Iravatham (b0060) 2003 Davies (b0035) 2017 Hao, S., & Li, S. (2020). A Weighted Mean Absolute Error Metric for Image Quality Assessment. Martín-Clemente, Hornillo-Mellado (b0095) 2006; 2006 88–101. https://uttamam.org/infitt_papers.php. Fu, Yang, Zeng, Zhang, Zhou (b0045) 2022; 11 Bhat, S., & Seshikala, G. (2018). Preprocessing and Binarization of Inscription Images using Phase Based Features. Zubair, Iqbal, Shil, Chowdhury, Moni, Sarker (b0200) 2022 Alginahi, Y. (2010). Preprocessing Techniques in Character Recognition. In Durga Devi, Uma Maheswari (b0040) 2019; 23 Niblack (b0120) 1985 Ntirogiannis, Gatos, Pratikakis (b0125) 2013; 22 Iravatham (b0065) 2014 Monisha, M., & Felix Enigo, V. S. (2020). Complexities in Developing Tamil-Brahmi Script OCR: An Analysis. 330–333. 10.1109/VCIP49819.2020.9301889. Zhang, J., & Hu, J. (2008). Image Segmentation Based on 2D Otsu Method with Histogram Analysis. Nair, B. J. B., Anusha, M. U., & Anusha, J. (2022). A Novel Stage Wise Denoising Approach on Ancient Kannada script from Rock Images. Shaus, Turkel, Piasetzky (b0155) 2012; 645 Kapur, Sahoo, Wong (b0085) 1985; 29 105–108. 10.1109/CSSE.2008.206. Xing, Ren (b0180) 2023; 11 Mousavi, S. M. H., & Muhammad Hassan Mosavi, S. (2022). A New Edge and Pixel-Based Image Quality Assessment Metric for Colour and Depth Images. Rogers, Zack (b0140) 1977; 25 1–6. 10.1109/ICAECC.2018.8479434. 1715–1723. 10.1109/ICCES54183.2022.9835997. Jyotsna, Chauhan, S., Sharma, E., & Doegar, A. (2016). Binarization techniques for degraded document images-A review. 163–166. 10.1109/ICRITO.2016.7784945. Sauvola, Pietikäinen (b0150) 2000; 33 Jain, V., Bollmann, B., Richardson, M., Berger, D. R., Helmstaedter, M. N., Briggman, K. L., Denk, W., Bowden, J. B., Mendenhall, J. M., Abraham, W. C., Harris, K. M., Kasthuri, N., Hayworth, K. J., Schalek, R., Tapia, J. C., Lichtman, J. W., & Seung, H. S. (2010). Boundary learning by optimization with topological constraints. Durga Devi (10.1016/j.eswa.2023.121320_b0040) 2019; 23 Bataineh (10.1016/j.eswa.2023.121320_b0010) 2017; 20 10.1016/j.eswa.2023.121320_b0100 Tran (10.1016/j.eswa.2023.121320_b0170) 2021; 30 10.1016/j.eswa.2023.121320_b0020 10.1016/j.eswa.2023.121320_b0185 10.1016/j.eswa.2023.121320_b0080 Bera (10.1016/j.eswa.2023.121320_b0015) 2021; 80 Iravatham (10.1016/j.eswa.2023.121320_b0060) 2003 Ntirogiannis (10.1016/j.eswa.2023.121320_b0125) 2013; 22 Chicco (10.1016/j.eswa.2023.121320_b0025) 2021; 7 Sauvola (10.1016/j.eswa.2023.121320_b0150) 2000; 33 Wang (10.1016/j.eswa.2023.121320_b0175) 2021; 20 Sukanthi (10.1016/j.eswa.2023.121320_b0165) 2021; 20 Fu (10.1016/j.eswa.2023.121320_b0045) 2022; 11 Kapur (10.1016/j.eswa.2023.121320_b0085) 1985; 29 Martín-Clemente (10.1016/j.eswa.2023.121320_b0095) 2006; 2006 10.1016/j.eswa.2023.121320_b0115 10.1016/j.eswa.2023.121320_b0055 Muller (10.1016/j.eswa.2023.121320_b0110) 2022; 11 Sreedevi (10.1016/j.eswa.2023.121320_b0160) 2013; 2013 Ramaneswaran (10.1016/j.eswa.2023.121320_b0130) 2021; 2021 10.1016/j.eswa.2023.121320_b0030 Davies (10.1016/j.eswa.2023.121320_b0035) 2017 10.1016/j.eswa.2023.121320_b0075 10.1016/j.eswa.2023.121320_b0090 Rogers (10.1016/j.eswa.2023.121320_b0140) 1977; 25 Iravatham (10.1016/j.eswa.2023.121320_b0065) 2014 10.1016/j.eswa.2023.121320_b0190 10.1016/j.eswa.2023.121320_b0070 Gatos (10.1016/j.eswa.2023.121320_b0050) 2006; 39 Saccenti (10.1016/j.eswa.2023.121320_b0145) 2020; 10 Zubair (10.1016/j.eswa.2023.121320_b0200) 2022 Shaus (10.1016/j.eswa.2023.121320_b0155) 2012; 645 Rasheed (10.1016/j.eswa.2023.121320_b0135) 2021; 1999 Zhu (10.1016/j.eswa.2023.121320_b0195) 2012; 7 Niblack (10.1016/j.eswa.2023.121320_b0120) 1985 10.1016/j.eswa.2023.121320_b0005 Xing (10.1016/j.eswa.2023.121320_b0180) 2023; 11 10.1016/j.eswa.2023.121320_b0105 |
| References_xml | – volume: 645 start-page: 645 year: 2012 end-page: 650 ident: b0155 article-title: Binarization of First Temple Period inscriptions - Performance of existing algorithms and a new registration based scheme publication-title: Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR – volume: 11 start-page: 55834 year: 2023 end-page: 55843 ident: b0180 article-title: Binary Inscription Character Inpainting Based on Improved Context Encoders publication-title: IEEE Access – volume: 10 start-page: 1 year: 2020 end-page: 19 ident: b0145 article-title: Corruption of the Pearson correlation coefficient by measurement error and its estimation, bias, and correction under different error models publication-title: Scientific Reports – volume: 39 start-page: 317 year: 2006 end-page: 327 ident: b0050 article-title: Adaptive degraded document image binarization publication-title: Pattern Recognition – volume: 11 start-page: 1 year: 2022 end-page: 13 ident: b0110 article-title: Application of Deep Learning Methods for Binarization of the Choroid in Optical Coherence Tomography Images publication-title: Translational Vision Science and Technology – volume: 1999 year: 2021 ident: b0135 article-title: The Effectiveness of the Finite Differences Method on Physical and Medical Images Based on a Heat Diffusion Equation publication-title: Journal of Physics: Conference Series – volume: 23 start-page: 2611 year: 2019 end-page: 2626 ident: b0040 article-title: Digital acquisition and character extraction from stone inscription images using modified fuzzy entropy-based adaptive thresholding publication-title: Soft Computing – year: 2003 ident: b0060 article-title: Early tamil epigraphy from the earliest times to the sixth century AD – volume: 2021 year: 2021 ident: b0130 article-title: Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification publication-title: Computational and Mathematical Methods in Medicine – reference: , 105–108. 10.1109/CSSE.2008.206. – volume: 2013 start-page: 1 year: 2013 end-page: 7 ident: b0160 article-title: NGFICA Based Digitization of Historic Inscription Images publication-title: ISRN Signal Processing – year: 2014 ident: b0065 article-title: Early tamil epigraphy from the earliest times to the sixth century C.E – reference: (10). 10.3390/jimaging8100272. – reference: Das, S., Mandal, S., & Das, A. K. (2016). Binarization of stone inscripted documents. – year: 2022 ident: b0200 article-title: An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling publication-title: Annals of Data Science – volume: 20 start-page: 4221 year: 2021 end-page: 4237 ident: b0175 article-title: A Minimum Error Probability NOMA Design publication-title: IEEE Transactions on Wireless Communications – reference: Nair, B. J. B., Anusha, M. U., & Anusha, J. (2022). A Novel Stage Wise Denoising Approach on Ancient Kannada script from Rock Images. – reference: , 330–333. 10.1109/VCIP49819.2020.9301889. – reference: , 2488–2495. 10.1109/CVPR.2010.5539950. – reference: , 163–166. 10.1109/ICRITO.2016.7784945. – volume: 22 start-page: 595 year: 2013 end-page: 609 ident: b0125 article-title: Performance evaluation methodology for historical document image binarization publication-title: IEEE Transactions on Image Processing – reference: Xu, W., Souly, N., & Brahma, P. P. (2021). Reliability of GAN Generated Data to Train and Validate Perception Systems for Autonomous Vehicles. – volume: 11 start-page: 1 year: 2022 end-page: 17 ident: b0045 article-title: Improvement of Oracle Bone Inscription Recognition Accuracy: A Deep Learning Perspective publication-title: ISPRS International Journal of Geo-Information – year: 1985 ident: b0120 article-title: An Introduction to Digital Image Processing – reference: Zhang, J., & Hu, J. (2008). Image Segmentation Based on 2D Otsu Method with Histogram Analysis. – volume: 7 start-page: 838 year: 2012 end-page: 841 ident: b0195 article-title: Application of improved median filter on image processing publication-title: Journal of Computers – reference: Jain, V., Bollmann, B., Richardson, M., Berger, D. R., Helmstaedter, M. N., Briggman, K. L., Denk, W., Bowden, J. B., Mendenhall, J. M., Abraham, W. C., Harris, K. M., Kasthuri, N., Hayworth, K. J., Schalek, R., Tapia, J. C., Lichtman, J. W., & Seung, H. S. (2010). Boundary learning by optimization with topological constraints. – volume: 25 start-page: 741 year: 1977 end-page: 753 ident: b0140 article-title: Automatic measurement of sister chromatid exchange frequency publication-title: The Journal of Histochemistry and Cytochemistry – reference: , – volume: 7 start-page: 1 year: 2021 end-page: 24 ident: b0025 article-title: The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation publication-title: PeerJ Computer Science – reference: . 10.1109/CFIS54774.2022.9756490. – reference: Monisha, M., & Felix Enigo, V. S. (2020). Complexities in Developing Tamil-Brahmi Script OCR: An Analysis. – reference: Hao, S., & Li, S. (2020). A Weighted Mean Absolute Error Metric for Image Quality Assessment. – reference: (Issue January). 10.5772/9776. – reference: J. Bernsen. (1986). Dynamic Thresholding of Gray Level Image. In – reference: Alginahi, Y. (2010). Preprocessing Techniques in Character Recognition. In – year: 2017 ident: b0035 article-title: Computer Vision, Fifth Edition: Principles, Algorithms, Applications, Learning – volume: 29 start-page: 273 year: 1985 end-page: 285 ident: b0085 article-title: A new method for gray-level picture thresholding using the entropy of the histogram publication-title: Computer Vision, Graphics, & Image Processing – reference: , 88–101. https://uttamam.org/infitt_papers.php. – volume: 80 start-page: 7653 year: 2021 end-page: 7673 ident: b0015 article-title: A non-parametric binarization method based on ensemble of clustering algorithms publication-title: Multimedia Tools and Applications – volume: 33 start-page: 225 year: 2000 end-page: 236 ident: b0150 article-title: Adaptive document image binarization publication-title: Pattern Recognition – reference: Lins, R. D., Bernardino, R., Barboza, R. da S., & De Oliveira, R. C. (2022). Using Paper Texture for Choosing a Suitable Algorithm for Scanned Document Image Binarization. – volume: 20 year: 2021 ident: b0165 article-title: Binarization of Stone Inscription Images by Modified Bi-level Entropy Thresholding publication-title: Fluctuation and Noise Letters – volume: 20 start-page: 639 year: 2017 end-page: 652 ident: b0010 article-title: Adaptive binarization method for degraded document images based on surface contrast variation publication-title: Pattern Analysis and Applications – reference: . – reference: , 1–6. 10.1109/ICAECC.2018.8479434. – reference: Mousavi, S. M. H., & Muhammad Hassan Mosavi, S. (2022). A New Edge and Pixel-Based Image Quality Assessment Metric for Colour and Depth Images. – volume: 2006 start-page: 502 year: 2006 end-page: 505 ident: b0095 article-title: Image processing using ICA: A new perspective publication-title: Proceedings of the Mediterranean Electrotechnical Conference - MELECON – volume: 30 start-page: 1882 year: 2021 end-page: 1897 ident: b0170 article-title: On Data Augmentation for GAN Training publication-title: IEEE Transactions on Image Processing – reference: Jyotsna, Chauhan, S., Sharma, E., & Doegar, A. (2016). Binarization techniques for degraded document images-A review. – reference: , 11–16. 10.1109/CGVIS.2015.7449883. – reference: , 171–180. 10.1109/WACVW52041.2021.00023. – reference: Bhat, S., & Seshikala, G. (2018). Preprocessing and Binarization of Inscription Images using Phase Based Features. – reference: , 1715–1723. 10.1109/ICCES54183.2022.9835997. – volume: 33 start-page: 225 issue: 2 year: 2000 ident: 10.1016/j.eswa.2023.121320_b0150 article-title: Adaptive document image binarization publication-title: Pattern Recognition doi: 10.1016/S0031-3203(99)00055-2 – ident: 10.1016/j.eswa.2023.121320_b0030 doi: 10.1109/CGVIS.2015.7449883 – volume: 645 start-page: 645 issue: 3 year: 2012 ident: 10.1016/j.eswa.2023.121320_b0155 article-title: Binarization of First Temple Period inscriptions - Performance of existing algorithms and a new registration based scheme publication-title: Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR – volume: 39 start-page: 317 issue: 3 year: 2006 ident: 10.1016/j.eswa.2023.121320_b0050 article-title: Adaptive degraded document image binarization publication-title: Pattern Recognition doi: 10.1016/j.patcog.2005.09.010 – volume: 25 start-page: 741 issue: 7 year: 1977 ident: 10.1016/j.eswa.2023.121320_b0140 article-title: Automatic measurement of sister chromatid exchange frequency publication-title: The Journal of Histochemistry and Cytochemistry doi: 10.1177/25.7.70454 – volume: 7 start-page: 1 year: 2021 ident: 10.1016/j.eswa.2023.121320_b0025 article-title: The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation publication-title: PeerJ Computer Science doi: 10.7717/peerj-cs.623 – volume: 10 start-page: 1 issue: 1 year: 2020 ident: 10.1016/j.eswa.2023.121320_b0145 article-title: Corruption of the Pearson correlation coefficient by measurement error and its estimation, bias, and correction under different error models publication-title: Scientific Reports doi: 10.1038/s41598-019-57247-4 – volume: 20 start-page: 639 issue: 3 year: 2017 ident: 10.1016/j.eswa.2023.121320_b0010 article-title: Adaptive binarization method for degraded document images based on surface contrast variation publication-title: Pattern Analysis and Applications doi: 10.1007/s10044-015-0520-0 – ident: 10.1016/j.eswa.2023.121320_b0080 doi: 10.1109/ICRITO.2016.7784945 – volume: 2021 year: 2021 ident: 10.1016/j.eswa.2023.121320_b0130 article-title: Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification publication-title: Computational and Mathematical Methods in Medicine doi: 10.1155/2021/2577375 – volume: 20 start-page: 4221 issue: 7 year: 2021 ident: 10.1016/j.eswa.2023.121320_b0175 article-title: A Minimum Error Probability NOMA Design publication-title: IEEE Transactions on Wireless Communications doi: 10.1109/TWC.2021.3056597 – volume: 23 start-page: 2611 issue: 8 year: 2019 ident: 10.1016/j.eswa.2023.121320_b0040 article-title: Digital acquisition and character extraction from stone inscription images using modified fuzzy entropy-based adaptive thresholding publication-title: Soft Computing doi: 10.1007/s00500-018-3610-2 – ident: 10.1016/j.eswa.2023.121320_b0075 doi: 10.1109/CVPR.2010.5539950 – year: 2017 ident: 10.1016/j.eswa.2023.121320_b0035 – volume: 2006 start-page: 502 year: 2006 ident: 10.1016/j.eswa.2023.121320_b0095 article-title: Image processing using ICA: A new perspective publication-title: Proceedings of the Mediterranean Electrotechnical Conference - MELECON – ident: 10.1016/j.eswa.2023.121320_b0020 doi: 10.1109/ICAECC.2018.8479434 – volume: 7 start-page: 838 issue: 4 year: 2012 ident: 10.1016/j.eswa.2023.121320_b0195 article-title: Application of improved median filter on image processing publication-title: Journal of Computers doi: 10.4304/jcp.7.4.838-841 – year: 1985 ident: 10.1016/j.eswa.2023.121320_b0120 – year: 2003 ident: 10.1016/j.eswa.2023.121320_b0060 – volume: 2013 start-page: 1 year: 2013 ident: 10.1016/j.eswa.2023.121320_b0160 article-title: NGFICA Based Digitization of Historic Inscription Images publication-title: ISRN Signal Processing doi: 10.1155/2013/735857 – volume: 80 start-page: 7653 issue: 5 year: 2021 ident: 10.1016/j.eswa.2023.121320_b0015 article-title: A non-parametric binarization method based on ensemble of clustering algorithms publication-title: Multimedia Tools and Applications doi: 10.1007/s11042-020-09836-z – volume: 1999 issue: 1 year: 2021 ident: 10.1016/j.eswa.2023.121320_b0135 article-title: The Effectiveness of the Finite Differences Method on Physical and Medical Images Based on a Heat Diffusion Equation publication-title: Journal of Physics: Conference Series – ident: 10.1016/j.eswa.2023.121320_b0115 – volume: 11 start-page: 55834 issue: June year: 2023 ident: 10.1016/j.eswa.2023.121320_b0180 article-title: Binary Inscription Character Inpainting Based on Improved Context Encoders publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3282442 – ident: 10.1016/j.eswa.2023.121320_b0190 doi: 10.1109/CSSE.2008.206 – volume: 30 start-page: 1882 year: 2021 ident: 10.1016/j.eswa.2023.121320_b0170 article-title: On Data Augmentation for GAN Training publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2021.3049346 – ident: 10.1016/j.eswa.2023.121320_b0070 – ident: 10.1016/j.eswa.2023.121320_b0055 doi: 10.1109/VCIP49819.2020.9301889 – ident: 10.1016/j.eswa.2023.121320_b0105 doi: 10.1109/CFIS54774.2022.9756490 – volume: 20 issue: 6 year: 2021 ident: 10.1016/j.eswa.2023.121320_b0165 article-title: Binarization of Stone Inscription Images by Modified Bi-level Entropy Thresholding publication-title: Fluctuation and Noise Letters doi: 10.1142/S0219477521500541 – ident: 10.1016/j.eswa.2023.121320_b0185 doi: 10.1109/WACVW52041.2021.00023 – ident: 10.1016/j.eswa.2023.121320_b0100 – volume: 11 start-page: 1 issue: 2 year: 2022 ident: 10.1016/j.eswa.2023.121320_b0110 article-title: Application of Deep Learning Methods for Binarization of the Choroid in Optical Coherence Tomography Images publication-title: Translational Vision Science and Technology doi: 10.1167/tvst.11.2.23 – year: 2022 ident: 10.1016/j.eswa.2023.121320_b0200 article-title: An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling publication-title: Annals of Data Science doi: 10.1007/s40745-022-00428-2 – volume: 11 start-page: 1 issue: 1 year: 2022 ident: 10.1016/j.eswa.2023.121320_b0045 article-title: Improvement of Oracle Bone Inscription Recognition Accuracy: A Deep Learning Perspective publication-title: ISPRS International Journal of Geo-Information doi: 10.3390/ijgi11010045 – year: 2014 ident: 10.1016/j.eswa.2023.121320_b0065 – volume: 22 start-page: 595 issue: 2 year: 2013 ident: 10.1016/j.eswa.2023.121320_b0125 article-title: Performance evaluation methodology for historical document image binarization publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2012.2219550 – volume: 29 start-page: 273 issue: 3 year: 1985 ident: 10.1016/j.eswa.2023.121320_b0085 article-title: A new method for gray-level picture thresholding using the entropy of the histogram publication-title: Computer Vision, Graphics, & Image Processing doi: 10.1016/0734-189X(85)90125-2 – ident: 10.1016/j.eswa.2023.121320_b0090 doi: 10.3390/jimaging8100272 – ident: 10.1016/j.eswa.2023.121320_b0005 doi: 10.5772/9776 |
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•To create standard Tamizhi dataset: capturing onsite inscriptions, collecting from various ASI, annotating the collected datasets by domain... |
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| SubjectTerms | Binarization Cave inscriptions Iterative thresholding Median filter Memorial stones Modified adaptive thresholding Tamizhi inscription images |
| Title | MLIBT: A multi-level improvised binarization technique for Tamizhi inscriptions |
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