Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
In the vast archives and libraries of the world, countless historical documents are tucked away, often difficult to access. Thankfully, the digitization process has made it easier to view these invaluable records. However, simply digitizing them is not enough – the real challenge lies in making them searchable and computer-readable. Many of these documents were handwritten, which means they need to undergo handwriting recognition. The first step in this process is to divide the document into lines. This article introduces a solution to this problem using tensor voting. The algorithm starts by conducting voting on the binary image itself. Then, using the local maxima found in the resulting tensor field, the lines of text are precisely tracked and labeled. To ensure its effectiveness, the algorithm’s performance was tested on the data-set delivered by the organizers of the ICDAR 2009 competition and evaluated using the criteria from this contest.
Rocznik
Tom
Strony
95--102
Opis fizyczny
Bibliogr. 46 poz., rys., tab.
Twórcy
autor
- Department of Computer Engineering, Wrocław University of Science and Technology, Wrocław, Poland
autor
- Department of Computer Engineering, Wrocław University of Science and Technology, Wrocław, Poland
Bibliografia
- [1] T. Babczyński and R. Ptak, “Line segmentation of handwritten documents using direct tensor voting,” in Dependable Computer Systems and Networks, W. Zamojski, J. Mazurkiewicz, J. Sugier, T. Walkowiak, and J. Kacprzyk, Eds. Cham: Springer Nature Switzerland, 2023, pp. 1-12.
- [2] L. Likforman-Sulem, A. Zahour, and B. Taconet, “Text line segmentation of historical documents: a survey,” International Journal of Document Analysis and Recognition (IJDAR), vol. 9, no. 2, pp. 123-138, 2007.
- [3] Z. Razak, K. Zulkiflee, M. Y. I. Idris, E. M. Tamil, M. Noorzaily, M. Noor, R. Salleh, M. Yaakob, Z. M. Yusof, and M. Yaacob, “Off-line handwriting text line segmentation: A review,” International Journal of Computer Science and Network Security, vol. 8, no. 7, pp. 12-20, 2008.
- [4] N. Mehta and J. Doshi, “Segmentation methods: A review,” International Journal for Research in Applied Science and Engineering Technology, vol. 8, pp. 536-540, 10 2020. [Online]. Available: doi:10.22214/ijraset.2020.31939
- [5] S. Joseph and J. George, “A review of various line segmentation techniques used in handwritten character recognition,” in Information and Communication Technology for Competitive Strategies (ICTCS 2021), A. Joshi, M. Mahmud, and R. G. Ragel, Eds. Singapore: Springer Nature Singapore, 2023, pp. 353-365.
- [6] B. Gatos, N. Stamatopoulos, and G. Louloudis, “ICDAR2009 handwriting segmentation contest,” International Journal on Document Analysis and Recognition (IJDAR), vol. 14, no. 1, pp. 25-33, 2011.
- [7] M. Shridhar and F. Kimura, “Handwritten address interpretation using word recognition with and without lexicon,” in 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems forthe 21st Century, vol. 3, 1995, pp. 2341-2346 vol.3. [Online]. Available: doi:10.1109/ICSMC.1995.538131
- [8] E. Kavallieratou, N. Dromazou, N. Fakotakis, and G. Kokkinakis, “An integrated system for handwritten document image processing,” International Journal of Pattern Recognition and Artifical Intelligence, vol. 17, pp. 617-636, 2003.
- [9] R. Ptak, B. Żygadło, and O. Unold, “Projection–based text line segmentation with a variable threshold,” International Journal of Applied Mathematics and Computer Science, vol. 27, no. 1, pp. 195-206, 2017.
- [10] T. Babczyński and R. Ptak, “Line segmentation of handwritten text using histograms and tensor voting,” International Journal of Applied Mathematics and Computer Science, vol. 30, no. 3, pp. 585-596, 2020. [Online]. Available: doi:10.34768/amcs-2020-0043
- [11] M. Arivazhagan, H. Srinivasan, and S. Srihari, “A statistical approach to line segmentation in handwritten documents,” in Document Recognition and Retrieval XIV, vol. 6500. International Society for Optics and Photonics, 2007, pp. 245-255.
- [12] T. Babczyński and R. Ptak, “Handwritten text lines segmentation using two column projection,” in Advances in Intelligent Systems and Computing. Springer, 2020, vol. 1173 AISC, pp. 11-20. [Online]. Available: doi:10.1007/978-3-030-48256-5{_}2
- [13] S. Han, M.-S. Lee, and G. Medioni, “Non-uniform skew estimation by tensor voting,” in Document Image Analysis, 1997.(DIA’97) Proceedings., Workshop on. IEEE, 1997, pp. 1-4.
- [14] G. Louloudis, B. Gatos, I. Pratikakis, and C. Halatsis, “Text line and word segmentation of handwritten documents,” Pattern Recognition, vol. 42, no. 12, pp. 3169-3183, 2009.
- [15] A. Alaei, P. Nagabhushan, and U. Pal, “Piece-wise painting technique for line segmentation of unconstrained handwritten text: a specific study with persian text documents,” Pattern Analysis and Applications, vol. 14, no. 4, pp. 381-394, 2011.
- [16] G. Louloudis, B. Gatos, I. Pratikakis, and C. Halatsis, “Text line detection in handwritten documents,” Pattern Recognition, vol. 41, no. 12, pp. 3758-3772, 2008.
- [17] L. Likforman-Sulem, A. Hanimyan, and C. Faure, “A hough based algorithm for extracting text lines in handwritten documents,” in Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on, vol. 2. IEEE, 1995, pp. 774-777.
- [18] Y. Pu and Z. Shi, “A natural learning algorithm based on hough transform for text lines extraction in handwritten documents,” Series in Machine Perception and Artificial Intelligence, vol. 34, pp. 141-152, 2000.
- [19] T. Nguyen Dinh and G. S. Lee, “Text line segmentation in handwritten document images using tensor voting,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E94.A, no. 11, pp. 2434-2441, 2011.
- [20] C. Zhang and G. S. Lee, “Text line segmentation in chinese handwritten text images,” in 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV), 2011, pp. 1-3.
- [21] D. J. Kennard and W. A. Barrett, “Separating lines of text in free-form handwritten historical documents,” in Second International Conference on Document Image Analysis for Libraries (DIAL’06), 2006, pp. 12-23. [Online]. Available: doi:10.1109/DIAL.2006.40
- [22] Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1124-1137, 2004.
- [23] Z. Shi, S. Setlur, and V. Govindaraju, “A steerable directional local profile technique for extraction of handwritten Arabic text lines,” in 10th International Conference on Document Analysis and Recognition, 2009, pp. 176-180. [Online]. Available: doi:10.1109/ICDAR.2009.79
- [24] J.-C. Wu, J.-W. Hsieh, and Y.-S. Chen, “Morphology-based text line extraction,” Mach. Vis. Appl., vol. 19, pp. 195-207, May 2008. [Online]. Available: doi:10.1007/s00138-007-0092-0
- [25] V. Papavassiliou, V. Katsouros, and G. Carayannis, “A morphological approach for text-line segmentation in handwritten documents,” in Frontiers in Handwriting Recognition (ICFHR), 2010 International Conference on. IEEE, 2010, pp. 19-24.
- [26] K. Y. Wong, R. G. Casey, and F. M. Wahl, “Document analysis system,” IBM journal of research and development, vol. 26, no. 6, pp. 647-656, 1982.
- [27] F. LeBourgeois, “Robust multifont ocr system from gray level images,” in Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on, vol. 1. IEEE, 1997, pp. 1-5.
- [28] S. Basu, C. Chaudhuri, M. Kundu, M. Nasipuri, and D. K. Basu, “Text line extraction from multi-skewed handwritten documents,” Pattern Recognition, vol. 40, no. 6, pp. 1825-1839, 2007.
- [29] D. Brodić and Z. Milivojević, “A new approach to water flow algorithm for text line segmentation,” Journal of Universal Computer Science, vol. 17, no. 1, pp. 30-47, 2011.
- [30] D. Brodić, “Extended approach to water flow algorithm for text line segmentation,” Journal of Computer Science and Technology, vol. 27, no. 1, pp. 187-194, 2012.
- [31] D. Brodić, “Text line segmentation with water flow algorithm based on power function,” Journal of Electrical Engineering, vol. 66, no. 3, pp. 132-141, 2015.
- [32] M. Feldbach and K. Tönnies, “Robust line detection in historical church registers,” in Pattern Recognition, 23rd DAGM-Symposium, 2001, pp. 140-147.
- [33] Y. Li, Y. Zheng, D. Doermann, and S. Jaeger, “Script-independent text line segmentation in freestyle handwritten documents,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 30, no. 8, pp. 1313-1329, Sep. 2008. [Online]. Available: doi:10.1109/TPAMI.2007.70792
- [34] J. S. Cardoso, A. Capela, A. Rebelo, and C. Guedes, “A connected path approach for staff detection on a music score,” in 2008 15th IEEE International Conference on Image Processing, 2008, pp. 1005-1008. [Online]. Available: doi:10.1109/ICIP.2008.4711927
- [35] T. Stafylakis, V. Papavassiliou, V. Katsouros, and G. Carayannis, “Robust text-line and word segmentation for handwritten documents images,” in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, pp. 3393-3396. [Online]. Available: doi:10.1109/ICASSP.2008.4518379
- [36] Q. N. Vo, S. H. Kim, H. J. Yang, and G. S. Lee, “Text line segmentation using a fully convolutional network in handwritten document images,” IET Image Processing, vol. 12, no. 3, pp. 438-446, 2018.
- [37] Y. Baek, B. Lee, D. Han, S. Yun, and H. Lee, “Character region awareness for text detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 9365-9374.
- [38] M.-S. Lee and G. Medioni, “Inferred descriptions in terms of curves, regions and junctions from sparse, noisy binary data,” in Proc. IEEE Int. Symp. Computer Vision, 1995, pp. 73-78.
- [39] E. Franken, M. van Almsick, P. Rongen, L. Florack, and B. ter Haar Romeny, “An efficient method for tensor voting using steerable filters,” in European Conference on Computer Vision. Springer, 2006, pp. 228-240.
- [40] G. Medioni and S. B. Kang, Emerging topics in computer vision. Upper Saddle River, N.J.: Prentice Hall PTR ; London : Pearson Education, 2004.
- [41] P. Mordohai and G. Medioni, “Tensor voting: A perceptual organization approach to computer vision and machine learning,” Synthesis Lectures on Image, Video, and Multimedia Processing, vol. 2, no. 1, pp. 1-136, 2006.
- [42] E. Maggiori, H. L. Manterola, and M. del Fresno, “Perceptual grouping by tensor voting: a comparative survey of recent approaches,” IET Computer Vision, vol. 9, no. 2, pp. 259-277, 2014.
- [43] T. Babczyński and R. Ptak, “Line segmentation of handwritten documents; the MATLAB code,” 2023. [Online]. Available: https: //github.com/tbabczynski-openSource/HWlinesSTV
- [44] I. T. Phillips and A. K. Chhabra, “Empirical performance evaluation of graphics recognition systems,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 849-870, 1999.
- [45] B. Gatos, N. Stamatopoulos, and G. Louloudis, “Icfhr 2010 handwriting segmentation contest,” in 2010 12th International Conference on Frontiers in Handwriting Recognition. IEEE, 2010, pp. 737-742.
- [46] N. Stamatopoulos, B. Gatos, G. Louloudis, U. Pal, and A. Alaei, “Icdar 2013 handwriting segmentation contest,” in 2013 12th International Conference on Document Analysis and Recognition. IEEE, 2013, pp. 1402-1406.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c85f1c1d-3d2d-4879-abe7-4420fe2cc71c