Abstract
This paper develops an algorithm that will help decipher the text in decrepit documents, which if put in simpler terms aims at converting stained, blotted, creased, and faded documents into a cleaner and legible format. Handwritten or printed records are carelessly stacked away without undertaking measures for preserving them. They are subjected to degradation because of mishandling and improper storage conditions. This ultimately results in the loss of important documentation owing to the inability of reproduction or recovery of original data. Digital image preprocessing techniques are used to convert a color (RGB) image into a grayscale image for further processing. Image denoising is one of the most sought areas after research in image processing, and in this paper, we use image segmentation and median filter to achieve this. In this paper, we attempted to come up with an approach to remove noise from the image by applying image segmentation and thresholding, histogram, and median filter.
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
Similar content being viewed by others
References
Reka, Durai, and V. Thiagarasu. 2014. A study and analysis on image processing techniques for historical document preservation. International Journal of Innovative Research in Computer and Communication 2 (7): 5195–5200.
Afrose, Zinat. 2012. A comparative study on noise removal of compound images using different types of filters. International Journal of Computer Applications (0975–888) 47 (14): 45–47.
Mallick, Satya. 2019. Image Inpainting with OpenCV. Available via https://www.learnopencv.com/image-inpainting-with-opencv-c-python/. Accessed 13 Apr 2019.
Vamvakas, G., B. Gatos, N. Stamatopoulos, S.J. Perantonis. 2008. A complete optical character recognition methodology for historical documents. Document Analysis Systems, IAPR International Workshop, 525–532. doi:10.1109/DAS.2008.73
Rajasekaran, Angalaparameswari, and P. Senthilkumar. 2014. Image denoising using median filter with edge detection using canny operator. International Journal of Science and Research 3 (2): 30–33.
Malothu, Nagu, and Shanker N. Vijay. 2014. Image de-noising by using median filter and weiner filter. International Journal of Innovative Research in Computer and Communication Engineering 2 (9): 5641–5645.
Sandeep, Kumar, Kumar Munish, and Agrawal Neha Rashid. 2017. A comparative analysis on image denoising using different median filter methods. International Journal for Research in Applied Science & Engineering Technology 5 (7): 231–238.
OpenCV Documentation, Available via http://docs.opencv.org/. Accessed 7 March 2017.
Suman, Shrestha. 2014. Image denoising using new adaptive based median filter. Signal & Image Processing: An International Journal (SIPIJ) 5 (4): 1–12.
Govindaraj, V., and G. Sengottaiyan. 2013. Survey of image denoising using different filters. International Journal of Science Engineering and Technology Research (IJSETR) 2 (2): 344–350.
Senthilkumaran, N., and S. Vaithegi. 2016. Image segmentation by using thresholding techniques for medical images. Computer Science & Engineering 6 (1): 1–6.
Sujata, Saini, and Arora Komal. 2014. A study analysis on the different image segmentation techniques. International Journal of Information & Computation Technology 4 (14): 1445–1452.
Jean-Luc, Starck, J. Candès Emmanuel, and Donoho David. 2002. The curvelet transform for image denoising. IEEE Transactions on Image Processing 11 (6): 670–684.
Julien, Mairal, Bach, Francis, Ponce, Jean. 2014. Sparse modeling for image and vision processing. arXiv 1411.3230: 76–97.
Priest, Colin. 2015. Denoising Dirty Documents. Available via https://colinpriest.com/2015/08/01/denoising-dirty-documents-part-1/. Accessed 27 Jan 2017.
Kaggle. 2015. Denoising Dirty Documents. Available via https://www.kaggle.com/c/denoising-dirty-documents. Accessed 13 April 2017.
Acknowledgements
The authors feel grateful and wish their profound indebtedness to their guide Prof. Milind Kamble, Department of Electronics and Telecommunication, Vishwakarma Institute of Technology, Pune. The authors also express their gratitude to Prof. Dr. R. M. Jalnekar, Director, and Prof. Dr. Shripad Bhatlawande, Head, Department of Electronics and Telecommunication, for their help in completion of the project. The authors also thank all the anonymous reviewers of this paper whose comments helped to improve the paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kulkarni, M., Kakad, S., Mehra, R., Mehta, B. (2020). Denoising Documents Using Image Processing for Digital Restoration. In: Swain, D., Pattnaik, P., Gupta, P. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1884-3_27
Download citation
DOI: https://doi.org/10.1007/978-981-15-1884-3_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1883-6
Online ISBN: 978-981-15-1884-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)