Segmentation of heterogeneous document images : an ... - Tel

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[25] C. Clausner, S. Pletschacher, and A. Antonacopoulos. Scenario driven in-depth performance evaluation of document layout analysis methods. In 11th International Conference on Document Analysis and Recognition (ICDAR ’11), pages 1404–1408. IEEE, 2011. [26] M. Collins. Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. The ACL02 conference on Empirical methods in natural language processingVolume 10, 10(July):1–8, 2002. [27] S. Deng, S. Latifi, and E. Regentova. Document segmentation using polynomial spline Wavelets. Pattern Recognition, 34(12):2533–2545, 2001. [28] D. Doermann. Document image understanding: integrating recovery and interpretation. PhD thesis, University of Maryland, College Park, 1993. tel-00912566, version 1 - 2 Dec 2013 [29] X. Du, W. Pan, and T. D. Bui. Text line segmentation in handwritten documents using Mumford-Shah model. Pattern Recognition, 42(12):3136– 3145, December 2009. [30] K. Etemad, D. Doermann, and R. Chellappa. Multiscale segmentation of unstructured document pages using soft decision integration. IEEE Transactions on Pattern Analysis and Machine Intelligence, l19(1):92–96, 1997. [31] P. F. Felzenszwalb and D. R. Huttenlocher. Efficient belief propagation for early vision. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2004 CVPR 2004, volume 1 of Int. J. Comput. Vis. (Netherlands), pages 41–54. Springer, 2004. [32] S. Ferilli, M. Biba, F. Esposito, and T. Basile. A distance-based technique for non-Manhattan layout analysis. 10th International Conference on Document Analysis and Recognition (ICDAR ’09), pages 231–235, July 2009. [33] L. A. Fletcher and R. Kasturi. A robust algorithm for text string separation from mixed text/graphics images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(6):910–918, 1988. [34] G. D. Forney. The Viterbi algorithm. IEEE, 61(3):42–52, 1973. [35] Y. Freund and R. Schapire. A desicion-theoretic generalization of online learning and an application to boosting. EuroCOLT ’95 Proceedings of the Second European Conference on Computational Learning Theory, 904(1):23–37, 1995. [36] J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. The Annals of Statistics, 28(2):337–407, 2000. [37] J. Friedman, T. Hastie, and R. Tibshirani. The elements of statistical learning, volume 1 of Springer Series in Statistics. Springer, 2001. [38] B. Gatos, A. Antonacopoulos, and N. Stamatopoulos. Handwriting segmentation contest. 9th International Conference on Document Analysis and Recognition (ICDAR ’07), 2:1284–1288, 2007. 120
[39] B. Gatos, A. Antonacopoulos, and N. Stamatopoulos. ICDAR2007 Handwriting segmentation contest. In 9th International Conference on Document Analysis and Recognition (ICDAR ’07), pages 1284–1288, Curitiba, Brazil, 2007. IEEE Computer Society. [40] B. Gatos, N. Stamatopoulos, and G. Louloudis. ICDAR2009 Handwriting segmentation contest. 10th International Conference on Document Analysis and Recognition (ICDAR ’09), 2011. [41] B. K. P. Horn. Shape from shading: a method for obtaining the shape of a smooth opaque object from one view. Technical report, Massachusetts Institute of Technology, Cambridge, MA, USA, 1970. [42] R. Jiang, F. Qi, L. Xu, G. Wu, and K. Zhu. A learning-based method to detect and segment text from scene images. Journal of Zhejiang University Science A, 8(4):568–574, 2007. tel-00912566, version 1 - 2 Dec 2013 [43] M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. Machine Learning, 233:183–233, 1999. [44] N. Journet, J. Ramel, R. Mullot, and V. Eglin. Document image characterization using a multiresolution analysis of the texture: application to old documents. International Journal of Document Analysis and Recognition (IJDAR ’08), 11(1):9–18, June 2008. [45] K. Khurshid. Analysis and retrieval of historical document images. PhD thesis, Universite Paris Descartes, 2009. [46] K. I. Kim, K. Jung, and J. H. Kim. Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12):1631–1639, December 2003. [47] K. Kise, M. Iwata, and K. Matsumoto. On the application of Voronoi diagrams to page segmentation. In Workshop on Document Layout Interpretation and its Applications (DLIA99), pages 6–9. Citeseer, 1998. [48] K. Kise, A. Sato, and M. Iwata. Segmentation of page images using the area Voronoi diagram. Computer Vision and Image Understanding, 70pp:370–382, 1998. [49] S. Kumar, R. Gupta, N. Khanna, S. Chaudhury, and S. D. Joshi. Text extraction and document image segmentation using matched wavelets and MRF model. IEEE Transactions On Image Processing, 16(8), 2007. [50] J. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional random fields: probabilistic models for segmenting and labeling sequence data. 8th International Conference on Machine Learning, pages(1):282–289, 2001. [51] A. H Land and A. G. Doig. An automatic method of solving discrete programming problems. Econometrica: Journal of the Econometric Society, 28(3):497–520, 1960. 121
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Resumé La segmentation de page est
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Acknowledgements This work would no
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4.3.2 Text components . . . . . . .
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3.6 Two documents that have obtaine
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6.1 PARAGRAPH DETECTION SUCCESS RAT
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detection and we conclude that the
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Figure 1.8: A screen shot that show
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Chapter 2 Related work tel-00912566
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them. In such circumstances, it wou
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[21] is another texture-based metho
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Figure 2.4: Part of a document in o
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• Degraded quality due to ageing
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2.3.2 Handwritten text line detecti
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(a) Divided strips and their projec
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(a) Five zones 1-5 (b) Projection p
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would be difficult to draw a conclu
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The proposed methods by Xiao [102],
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tel-00912566, version 1 - 2 Dec 201
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is assigning a label to a region of
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fixed range. When the elongation ap
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The second method calculates the co
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3. Repeat for m = 1, 2, ..., M •
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Chapter 4 Region detection tel-0091
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The next advantage of using CRFs is
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weights that are assigned to edge a
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{ 1 if ys = text and y f 1 (y s , y
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(a) Document (b) Filled text compon
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f = [y c = 0] × [y tl = 0] f = [y
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(a) Ground-truth (b) y c = 0 tel-00
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Page 81 and 82: incorrect [100]. Several sufficient
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Page 109 and 110: text lines can be divided into two
Page 111 and 112: the two children. The root node rep
Page 113 and 114: leaves of the tree which contain on
Page 121 and 122: currently working on some of these
Page 123 and 124: • fn (false negative) is the numb
Page 125 and 126: 2 ∗ RA ∗ DR F − Measure = RA
Page 127 and 128: • ”-tn”: This option uses the
Page 129: [12] T. M. Breuel. Two geometric al
Page 133 and 134: [64] K. P. Murphy, Y. Weiss, and M.
Page 135 and 136: [91] M. Stamp. A revealing introduc
Page 137 and 138: Index tel-00912566, version 1 - 2 D
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