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

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tel-00912566, version 1 - 2 Dec 2013 Figure 2.9: Images [62] show line segmentation steps. a A rotated image. b Vertical projection profile of the rotated image. c Smoothed version of the vertical projection profile. d Detected text line separators by detecting peaks in the smoothed projection profile. (a) Part of a handwritten manuscript (b) ALCM (c) Binarization of ALCM (d) Detected text lines Figure 2.10: Text line detection method in [87] using adaptive local connectivity map. 28
(a) Divided strips and their projection profiles (b) Extracted blocks Figure 2.11: Block extraction steps in [105] tel-00912566, version 1 - 2 Dec 2013 method works on blocks. But the difference is that the authors assume two different types of documents, namely tightly spaced documents (TSD) and widely spaced documents (WSD) to better cope with overlapping and multi-touching components. Figure 2.11 shows the projection profiles for several strips and the result of their block extraction. For each block, the method computes two features based on fractal dimensions resulting from the classical box-counting algorithm. Then an unsupervised fuzzy C-means 2-class classifier is used to separate blocks into tightly packed or widely spaced. Each block type is approached differently for detecting text lines. Another method is proposed by Arivazhagan et al. in [7] that starts by obtaining candidate lines using piecewise vertical projection profiles similar to the one used in [105]. Then the method draws a decision for any obstructing element based on the bivariate Gaussian probability density of a distance metric to find out whether the element belongs to the line above or the line below. After applying the piecewise projection profile to the document image, the complete set of text line separators are drawn by connecting a valley of a profile associated with a block on the right, to a valley from the block on its left and continuing the line straightly in a situation where a valley is still unconnected. For each connected component in which a line passes through, it may belong totally to either the line above or the live below, or it may need to be broken into two components. The method uses a distance metric decision to determine which approach should be taken for each obstructing component. The last reviewed projection based method is proposed by Papavassiliou et al. [75]. This method is very similar to the one in [7] with one difference that it adds another stage based on Hidden Markov Models (HMM) to correct some misleading peaks and valleys of projection profiles and therefore, it segments vertical strips into better situated blocks compared to other methods. Initially, like other methods, text and gap areas are extracted by detecting peaks and valleys in a smoothed projection profile computed for each vertical strip. Then a Viterbi algorithm locates the optimal succession of text and gap areas based upon the statistics drawn from the initial set of blocks. Finally, a text line separating technique is applied to assign connected components into appropriate text lines. Results for preliminary version of this method are published in [90] and submitted to the ICDAR2007 [38] and ICDAR2009 [40] handwritten 29
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Page 3 and 4: Resumé La segmentation de page est
Page 5 and 6: Acknowledgements This work would no
Page 7 and 8: 4.3.2 Text components . . . . . . .
Page 9 and 10: 3.6 Two documents that have obtaine
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Page 21 and 22: Figure 1.8: A screen shot that show
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Table 4.3: TION COUNT WEIGHTED SUCC
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Chapter 5 Text line detection tel-0
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Having specified the model, a verti
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• The fifth step is to remove ext
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text lines can be divided into two
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the two children. The root node rep
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leaves of the tree which contain on
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currently working on some of these
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• fn (false negative) is the numb
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2 ∗ RA ∗ DR F − Measure = RA
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• ”-tn”: This option uses the
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[12] T. M. Breuel. Two geometric al
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[39] B. Gatos, A. Antonacopoulos, a
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[64] K. P. Murphy, Y. Weiss, and M.
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[91] M. Stamp. A revealing introduc
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Index tel-00912566, version 1 - 2 D
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Segmentation of heterogeneous document images : an ... - Tel

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