Abstract
Denoising in epigraphical document analysis helps in building recognition system for fast and automatic processing. However, it is challenging due to the presence of stone texture as a complex background in input samples. In this paper, a nested run length counting with varying block size of 3 * 3, 5 * 5 and 7 * 7 are applied. Computation is carried out on neighboring pixels of the point of interest and discloses whether it is part of the script on inscription or background based on the count value. If it is part of the background, point of interest is set to background value else set to white. The method is tried and tested on 100 samples of epigraphical Estampages collected from archaeological survey of India. A comparative study is derived on the output of the proposed method and on the nonlinear filters such as median and wiener. Human vision perception has evaluated that proposed method is better than median and wiener filters. The quality measures such as Peak signal to noise ratio and Structural similarity indexes are practiced on the sample output for various filters and proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baig, A., Al-Ma’adeed, S.A.S., Bouridane, A., Cheriet, M.: Automatic segmentation and reconstruction of historical manuscripts in gradient domain. IET Image Process. 12, 502–512 (2018)
Gangamma, B., Murthy, K.S., Singh, A.V.: Restoration of degraded historical document image. J. Emerg. Trends Comput. Inf. Sci. 3(5), 792–798 (2012)
Zhang, J., et al.: Denoising of Chinese calligraphy tablet images based on run-length statistics and structure characteristic of character strokes. J. Zhejiang Univ. Sci. 7, 1176–1186 (2006)
Karthik, S., Mamatha, H.R., Murthy, K.S.: An approach based on run length count for denoising the kannada characters. Int. J. Comput. Appl. 50(18), 42–46 (2012)
Al-Najjar, Y.A.Y., Soong, D.C.: Comparison of image quality assessment: PSNR, HVS, SSIM, UIQI. Int. J. Sci. Eng. Res. 3(8), 118–126 (2012)
Boudraa, O., Hidouci, W.K., Michelucci, D.: A robust multi stage technique for image binarization of degraded historical documents. In: 5th International Conference on Electrical Engineering. IEEE (2017)
Chairy, A., et al.: Image restoration on copper inscription using nonlinear filtering and adaptive threshold. J. Phys: Conf. Ser. 801, 012043 (2017)
Kavallieratou, E., Stamatatos, E.: Improving the quality of degraded document images. In: The Second International Conference on Document Image Analysis for Libraries (DIAL 2006). IEEE (2006)
Janani, G., Vishalini, V., Kumar, P.M.: Recognition and analysis of Tamil inscriptions and mapping using image processing techniques. In: International Conference on Science Technology Engineering and Management. IEEE (2016). ISBN 978-1-5090-1706-5
Elfattah, M.A., Hassanien, A.E., Mostafa, A., Ali, A.F.: Artificial bee colony optimizer for historical arabic manuscript images binarization. In: 2015 11th International Computer Engineering Conference. IEEE (2015). ISBN 978-1-5090-0275-7
Chakraborty, A., Blumenstein, M.: Preserving text content from historical handwritten documents. In: 2016 12th IAPR Workshop on Document Analysis Systems. IEEE (2016). ISBN 978-1-5090-1792-8
Horé, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition DBLP (2010)
Soloman, R.: Indian Epigraphy, 2nd edn. Oxford Indian Press (1999). ISBN 0-19-509984-2
Keiran, G.: Structural similarity index simplified. Occasional Texts in the Pursuit of Clarity and Simplicity in Research. Series 1, no. 1 (2015)
Acknowledgement
My sincere thanks to The Director and staff members of Archaeological survey of India, Mysore for providing data samples for the research work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Preethi, P., Praneeth Kumar, K., Sumukha, M., Mamatha, H.R. (2019). Denoising Epigraphical Estampages Using Nested Run Length Count. In: Kumar, N., Venkatesha Prasad, R. (eds) Ubiquitous Communications and Network Computing. UBICNET 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 276. Springer, Cham. https://doi.org/10.1007/978-3-030-20615-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-20615-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20614-7
Online ISBN: 978-3-030-20615-4
eBook Packages: Computer ScienceComputer Science (R0)