Character Restoration in Degraded Documents Using Hybrid Neuron Fuzzy Approach

Harshmani*, Nancy Gupta**, Gurpreet Kaur***
* PG Scholar, Department of Electronics and Communication Engineering, I.K. Gujral Punjab Technical University, Jalandhar, India.
**-*** Assistant Professor, Department of Electronics and Communication Engineering, CTIEMT, Shahpur, Jalandhar, India.
Periodicity:July - September'2016

Abstract

Ancient documents may contain the valuable information of our historical past, which might be available in the printed or handwritten form. The text preservation of these antique documents has a vital significance for future generation and references. But due to several degradation factors such as bleeding through, shadow through, ink bleeding, paper aging, etc., documents are unable to show their contents up to the mark. There are various restoration techniques that may serve this purpose, but due to non-linear and complex nature of the degrading parameters, the existing techniques turn out to be less promising. The aim of study is to investigate the capability of ANN & Fuzzy logic, i.e. 'Neuro-Fuzzy technique' to restore the historical documents from their digital images. In the proposed technique, the Back- Propagation Neural Network (BPNN) is trained to cope with different degrading factors and Fuzzy rules are used to further suppress leftover spurious pixels. The output results of the proposed technique on different degraded document images are presented and compared with various existing techniques, viz. Otsu, Sauvola, Wolf, Niblack, Bernsen and Maximum entropy. The comparative results show the superiority of the proposed technique, which outperforms all other comparative techniques by providing visually better output images.

Keywords

Degraded Documents, Character Restoration, Neuro-Fuzzy, BPNN.

How to Cite this Article?

Harshmani, Gupta, N., and Kaur, G. (2016). Character Restoration in Degraded Documents Using Hybrid Neuron Fuzzy Approach. i-manager's Journal on Image Processing, 3(3), 27-33.

References

[1]. Kaur, Rupinder, et al., (2014). “A Novel Image Restoration Algorithm For Digitized Degraded Historical Documents”. International Journal of Science, Engineering and Technology Research, Vol. 3, No 9.
[2]. Kanungo, T. (1996). “Document degradation models and a methodology for degradation model validation”. (Doctoral Dissertation, University of Washington).
[3]. Su, Bolaun, et al., (2012). “A Robust Document Image Binarization Technique for Degraded Document Images”. IEEE Transaction on Image Processing, Vol. 22, pp. 1408- 1417.
[4]. Kaur, Jagroop, et al., (2014). “Improved Degraded Document Image Binarization Using Guided Image Filter”. IJARCSSE, Vol. 4, No. 9.
[5]. Arney, J.S, et al., (1979). “Accelerated Aging of Paper: The Relative Importance of Atmospheric Oxidation”. Tappi, Vol. 62, No. 7, pp. 89-91.
[6]. Banik, G., Sobotka, W.K., Vendl, A., and Norzsicska, S. (1993). “Effects of Atmospheric Pollutants on Deacidified Modern Papers”. In (Bridgland, J., Ed.) 10th Triennial Meeting, Washington DC, USA, pp. 435-441.
[7]. Kefali, Abderrahane, et al., (2014). “Foreground Background separation by Feed forward neural network in old Manuscripts”. Informatica, Vol. 38, pp 329-338.
[8]. R. C. Gonzalez and R. E. Woods, (2007). Digital Image Processing. Prentice Hall.
[9]. N. Otsu. (1979). “A Threshold Selection Method from Gray-Level Histogram”. IEEE Trans. Systems, Man, and Cybernetics, Vol. 9, pp. 62-66.
[10]. Kapur, J.N., Sahoo, P.K., and Wong, A.K.C. (1985). “A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram”. Computer Vision, Graphics, and Image Processing, Vol. 29, pp.273-285.
[11]. Solihin, Yan., and Leedham, C.G. (1999). “Integral Ratio: A New Class of Global Thresholding Techniques for Handwriting Images”. IEEE Trans on Pattern Analysis and Machine Intelligence, Vol. 21, pp. 761-768.
[12]. Graham Leedham, et al., (2002). “Separating Text and Background in Degraded Document Images- A Comparison of Global Thresholding Techniques for Multi- Stage Thresholding”. Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition IEEE, pp. 244-249.
[13]. Vala, Hetal J., and Baxi, Astha, (2013). “A review on Otsu image segmentation algorithm”. IJARCET, Vol. 2, No. 2.
[14]. Kumar, C Arun, et al., (2014). “Content Restoration of termite bitten degraded documents”. International Journal of Engineering and Research Applications, Vol. 4, No. 5, pp. 151-155.
[15]. Sauvola, J., Seppanen, T., Haapakoski, and S., Pietikainen, M. (1997). “Adaptive Document Binarization”. 4th Int. Conf. on Document Analysis and Recognition, Ulm, Germany, pp.147-152.
[16]. Ramirej-Ortegon, M A, et al., (2010). “Transition pixel: A concept for Binarization based on Edge detection and Gray Intensity histograms”. Pattern Recognition, Vol. 43, No. 4, pp. 1233-1243.
[17]. Meng-Ling Feng and Yap-Peng Tan, (2004). “Contrast adaptive binarization of low quality document images”. IEICE Electron. Express, Vol. 1, No. 16, pp. 501- 506.
[18]. Ergina Kavallieratou and Stamatatos Stathis, (2006). “Adaptive Binarization of Historical Document Images”. th The 18 International Conference on Pattern Recognition IEEE, Vol. 3, pp. 742-745.
[19]. Yuasa, Kenichieo, et al., (1996). “Restoration of Degraded Character Dot Image Using Discrete Hopfield Neural Network”. Digital Signal Processing Workshop, IEEE. pp. 287-290.
[20]. Yu Qiao, et al., (2006). “A framework toward Restoration of writing order from single–stroked Handwriting image”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 11, pp. 1724-1737.
[21]. Zhenwen Dai and Jorg Lucke, (2014). “Autonomous Document cleaning- A generative approach to reconstruct strongly corrupted scanned texts”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 10, pp 1950-1962.
[22]. Quraishi, M.I., et al., (2013). “A novel hybrid approach to restore historical degraded documents”. International Conference on Intelligent Systems and Signal Processing (ISSP), IEEE, pp. 185-189.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.