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Abstrakty
Character segmentation (i.e. splitting the images of handwritten words into pieces corresponding to single letters) is one of the required steps in numerous off-line cursive handwritten word recognition solutions. It is also a very important step, because improperly extracted characters are usually impossible to recognize correctly with currently used methods. The most common method of character segmentation is initial oversegmentation - finding some set of potential splitting points in the graphical representation of the word and then attempting to eliminate the improper ones. This paper con- tains a list of popular approaches for generating potential splitting points and methods of verifying their correctness.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
43-65
Opis fizyczny
Bibliogr. 29 poz., rys.
Twórcy
autor
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Reymonta 4, 30-059 Kraków, Poland, m.dudek@uj.edu.pl
Bibliografia
- [1] Casey R.G., Lecolinet E.; A Survey of Methods and Stratiegies in Character Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7), 1996, pp. 690–706.
- [2] Plamondon R., Srihari S.N.; On-Line and Off-Line Handwritting Recognition: A Comprehensive Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 2000, pp. 63–84.
- [3] Lu Y., Shridhar M.; Character segmentation in Handwritten Words – An Overview, Pattern Recognition, 29, 1996, pp. 77–96.
- [4] Verma B.; A Contour Code Feature Based Segmentation For Handwriting Recognition, Proceedings of the Seventh International Conference on Document Analysis and Recognition, 2, 2003, pp. 1203.
- [5] Zheng L., Hassin A.H., Tang X.; A new algorithm for machine printed Arabic character segmentation, Pattern Recognition Letters, 25, 2004, pp. 1723-–1729.
- [6] Nicchiotti G., Scagliola C.; Generalised Projections: a Tool for Cursive Handwriting Normalisation, International Conference on Document Analysis and Recognition, 5, 1999, pp. 729.
- [7] Kavallieratou E., Fakotakis N., Kokkinakis G.; A slant removal algorithm, Pattern Recognition, 33, 2000, pp. 1261–1262.
- [8] Leedham C.G., Friday P.D.; Isolating individual handwritten characters, Proc. IEE Colloq. Character Recognition and Applications, 1989, pp. 4/1–4/7.
- [9] Yanikoglu B., Sandon P.A.; Segmentation of off-line cursive handwriting using linear programming, Pattern Recognition, 31(12), 1998, pp. 1825-–1833.
- [10] Zeeuw G. de; Slant Correction using Histograms, Bachelor’s thesis, University of Groningen, 2006. Available via http://www.ai.rug.nl/~axel/teaching/bachelorprojects/zeeuw_slantcorrection.pdf.
- [11] Madhvanath S., Kim G., Govindaraju V.; Chaincode Contour Processing for Hand-written Word Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(9), 1999, pp. 928–932.
- [12] Nicchiotti G., Scagliola C., Rimassa S.; A Simple And Effective Cursive Word Segmentation Method, Proceedings of the Seventh International Workshop on Frontiers in Handwriting Recognition, 2000, pp. 11–13.
- [13] Morita M., Lethelier E., Yacoubi A. El, Bortolozzi F., Sabourin R.; An HMM-based Approach for Date Recognition, Proceedings of the Fourth IAPR International Workshop on Document Analysis Systems, 2000.
- [14] Bozekova M.; Comparison of Handwritings, Diploma thesis, Comenius University, 2008.
- [15] Oliveira L.S.; Automatic Recognition of Handwritten Numerical Strings, PhD thesis, Ecole de Technologie Superieure, 2003.
- [16] Jang B.K., Chin R.T.; One-Pass Parallel Thinning: Analysis, Properties, and Quantitative Evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(11), 1992, pp. 1129–1140.
- [17] Huang L., Wan G., Liu C.; An Improved Parallel Thinning Algorithm, Proceedings of the Seventh International Conference on Document Analysis and Recognition, 2003, pp. 780–783.
- [18] Zhao S., Chi Z., Shi P., Yan H.; Two-stage segmentation of unconstrained handwritten Chinese characters, Pattern Recognition, 36, 2003, pp. 145–156.
- [19] Liang Z., Shi P.; A metasynthetic approach for segmenting handwritten Chinese character strings, Pattern Recognition Letters, 26, 2005, pp. 1498–1511.
- [20] Lu Z., Chi Z., Siu W., Shi P.; A background-thinning-based approach for separating and recognizing connected handwritten digit strings, Pattern Recognition, 32, 1999, pp. 921–933.
- [21] Xiao X., Leedham G.; Knowledge-based English cursive script segmentation, Pattern Recognition Letters, 21, 2000, pp. 945–954.
- [22] Verma B.; A Contour Code Feature Based Segmentation For Handwriting Recognition, Proceedings of the Seventh International Conference on Document Analysis and Recognition, 2003, pp. 1203.
- [23] Kavallieratou E., Fakotakis N., Kokkinakis G.; An unconstrained handwriting recognition system, International Journal on Document Analysis and Recognition, 4(4), 2002, pp. 226–242.
- [24] Blumenstein M., Verna B.; A New Segmentation Algorithm for Handwritten Word Recognition, Proceedings of the International Joint Conference on Neural Networks, 1999, pp. 2893–2898.
- [25] Vellasques E., Oliveira L.S., Britto A.S. Jr., Koerich A.L., Sabourin R.; Filtering segmentation cuts for digit string recognition, Pattern Recognition, 41(10), 2008, pp. 3044–3053.
- [26] Madhvanath S., Govindaraju V.; The Role of Holistic Paradigms in Handwritten Word Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 2001, pp. 149–164.
- [27] Vinciarelli A.; A survey on off-line Cursive Word Recognition, Pattern Recognition, 35, 2002, pp. 1433–1446.
- [28] Guillevic D.; Unconstrained handwriting recognition applied to the processing of bank cheques, PhD thesis, Concordia University, 1995.
- [29] Liu Z.-Q., Cai J., Buse R.; Handwriting Recognition, Soft Computing and Probabilistic Approaches, Springer, 2003.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-article-BUJ8-0023-0002