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Reseach Article

Text Reconstruction using Torn Document Mosaicing

by Kantilal P.Rane, S.G.Bhirud
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 30 - Number 10
Year of Publication: 2011
Authors: Kantilal P.Rane, S.G.Bhirud
10.5120/3669-5170

Kantilal P.Rane, S.G.Bhirud . Text Reconstruction using Torn Document Mosaicing. International Journal of Computer Applications. 30, 10 ( September 2011), 21-27. DOI=10.5120/3669-5170

@article{ 10.5120/3669-5170,
author = { Kantilal P.Rane, S.G.Bhirud },
title = { Text Reconstruction using Torn Document Mosaicing },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 30 },
number = { 10 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 21-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume30/number10/3669-5170/ },
doi = { 10.5120/3669-5170 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:16:45.422009+05:30
%A Kantilal P.Rane
%A S.G.Bhirud
%T Text Reconstruction using Torn Document Mosaicing
%J International Journal of Computer Applications
%@ 0975-8887
%V 30
%N 10
%P 21-27
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Text extraction from torn documents is a critical process in the field of document reconstruction. Estimation of fragment orientation based on the text orientation is proposed to get an exact rotational angle with x axis. Corners detection with extraction of the pick points is introduced as a basic feature for the boundary matching process. A novel approach of single matched corner identification for finding the corresponding points between two oriented fragments is implemented. Dilation process is used to extract the background of the text from the text image. Removal of background from text image is devised for the clear visualization of seamless text with black or white background which is responsible for making the merging process easy. Shift and Merge method is implemented for positioning of the matched fragments. A function called as a maskblending is utilized to merge the extracted texts from the shifted fragments along masked irregular shape. An experimental result clearly visualizes the different steps of document mosaicing.

References
  1. H. C. da Γ Leitao and J. Stolfi. A multiscale method for th reassembly of two dimensional fragmented objects. IEEE Trans. Pattern Anal.Mach. Intell., 24(9):1239-1251, 2002.
  2. B. Burden and H.Wolfson, solving jigsaw puzzles by a robot, Robotics and Automation, IEEE Transactions on, 5(6):752-764, Dec 1989.
  3. M. Prandtstetter and G.H.Raidl, “Meta-heuristics for reconstructing cross cut shredded Text Documents,” Proceedings of Genetic and Evolutionary Computation Conference (GECCO’09), 2009.
  4. M. Sagiroglu and A.Ercil. A texture based matching approach for automated assembly of puzzles. 18th Int. Conf. on Pattern Recognition (ICPR06), 3, pp: 1036-1041,2006.
  5. Ukovich and G.Ramponi. Features for the reconstruction of shredded notebook paper, IEEE Int. Conf. on Image Processing (ICIP’05), 3:III-93-6. Sept. 2005.
  6. M. Prandtstetter and G.H.Raidl, “Combining to reconstruct strip shredded text documents,” HM 08: Proceedings of the 5th International Workshop on Hybrid Metaheuristics. Berlin, Heidelberg: Springer-Verlog, 2008, pp.1235-1239, 2001.
  7. F. Kleber, M. Diem and R. Sablatnig, “Torn Document Analysis as a Prerequisite for Reconstruction,” 15th IEEE Int. Conf. on Virtual Systems and Multimedia, pp.143-148, 2009.
  8. Anh-Nga Lai, Hyosun Yoon, Gueesang Lee, “Robust background extraction scheme using histogram-wise for real-time tracking in urban traffic video”, 8th IEEE International Conference on Computer and Information Technology, PP-845-850, July 2008.
  9. Xue Mei, Ramachandran, M., Shaohua Kevin Zhou, “Video Background Retrieval using Mosaic Images”, International Conference on Acoustics, Speech, and Signal Processing, 2005, Volume: 2, PP: 441- 444, March 2005.
  10. Yasuaki Sakai, Joo Kooi, Seiji Ishikawa, “Extracting a Human Area by Background Detection”, IEEE SICE-ICAS International Joint Conference 2006 in Bexco, Korea, pp-2296-2299, 2006.
  11. van den Boomgard, R, and R. van Balen, "Methods for Fast Morphological Image Transforms Using Bitmapped Images," Computer Vision, Graphics, and Image Processing: Graphical Models and Image Processing, Vol. 54, Number 3, pp. 254-258, May 1992.
  12. Weixing Wang, “Image segmentation of irregular shape grains on ceramic material surfaces”, International Conference on Computer Graphics, Imaging and Vision: New Trends, pp- 49 - 54, 2005.
  13. Chen, C. & Stamos, I., “Range Image Segmentation for Modeling and Object Detection in Urban Scenes”, 6th International Conference on 3-D Digital Imaging and Modeling, Montreal, Canada, August 21-23, 2007.
  14. Christian Barat, Benoit Lagadec, “A Corner Tracker Snake Approach to Segment Irregular Object Shapein Video Image”, IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas: United States, 2008.
  15. Kyoko Nakamura, Mitsuharu Ohki, Takashi Totsuka, “Image blending by Feature Overwrite”, IEEE International Conference, pp- 226-230, 1998.
  16. Ming-Shing, Wen-Liang Hwang, Kuo-Young Cheng, “Analysis on Multiresolution Masaic Images”, IEEE Transactions on Image Processing, Vol.13 No.7, July 2004.
  17. Fan Gu, Yuri Rzhanov, “Optimal Image Blending for Underwater Mosaic”, IEEE International Conference, 2006.
  18. Yingen Xiong, kari Pulli, “Fast and High-Quality Image Blending on Mobile Phones”, IEEE CCNC 2010, 2010.
  19. “Masked Irregular Shape Blending”, http:// www.eecis.udel.edu/~qili/ta/cis489/2/.
  20. Pietro Perona and Jitendra Malik, "Scale-space and edge detection using anisotropic diffusion," Proceedings of IEEE Computer Society Workshop on Computer Vision, pp. 16–22, November 1987.
  21. “Marr Hildreth Edge Detector”, http://en.wikipedia.org /wiki/Marr-Hildreth_algorithm/
  22. P.V.C. Hough. Method and means for recognizing complex patterns, u.s. patent 3069654. 1962.
  23. C. Galambos, J. Kittler, and J. Matas, “Progressive probabilistic hough transform for line detection,” Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 1:1554, 1999.
  24. A Richard O. Duda and Peter E. Hart. Use of the hough transformation to detect lines and curves in pictures. Commun. ACM, 15(1):11–15, January 1972.
  25. “L*a*b* color spaces”, http://en.wikipedia.org/ wiki/Lab_color_space#cite_note-11.
  26. Nae-Joung Kwak, Dong-Jin Kwon, Young-Gil Kim, Jae-Hyeong Ahn, “Color image segmentation using edge and adaptive threshold value based on the image characteristics,” Book (ISBN: 0-7803-8639-6), pp: 555 – 558, Nov. 2004.
  27. “RGB color spaces to L*a*b* color spaces Transformations”,http://en.wikipedia.org/wiki/Lab_color_space.
  28. “8-Connected neighborhood boundary detection technique”, http://www.mathwork/fileexchange/ boundary.
  29. S. Belongic, J. Malik, “Shape Matching and object recognition using Shape Contexts,” IEEE Trans. on pattern Analysis and Machine Intelligence, Vol.20, N0. 24, April 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Document Reconstruction Text Extraction Corner Detection Boundary matching