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

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tel-00912566, version 1 - 2 Dec 2013 (a) Image (b) Otsu binarization (c) Sauvola binarization Figure 3.2: Sauvola’s binarization method breaks the integrity of graphical components and drawings in healthy documents and produces many small connected components that may resemble text characters. This image is part of a document from ICDAR2009 dataset. 38
is assigning a label to a region of the image, it tends to extract features based on texture analysis or projection profiles. The method we describe here is a hybrid approach for separating text from graphics. It is designed to assign a label to one connected component but features are gathered based on both the characteristics of the components and the components in its neighborhood. tel-00912566, version 1 - 2 Dec 2013 We consider 16 features as potential features for the purpose of separating text and graphics components. Each feature may help to discriminate components in a particular situation. The first three features are computed relative to other components of a page. Using these three features, the aim is to estimate how likely the current component is different from other components on the same page in regard to its elongation, height or solidity. The rest of the features are global features, extracted from all components of the pages from our training dataset. Height and width of a component are equal to height and width of the bounding box of the component. Elongation and solidity are defined as follows: and solidity = Considered features are: elo = min(height, width) max(height, width) Number of pixels of the component height × width • Log-normal distribution of 1/solidity 1 . For most of characters, the number of black pixels divided by the area of their bounding box, namely solidity, is located in a fixed range. This property does not hold on tables, borders and many graphical drawings. Looking for outlier components of the page regarding this criterion may help in classifying them as non-textual components. Figure 3.3 shows histograms of 1/solidity and log(1/Solidity) for text components on the whole corpus which visually justify the use of Log-normal distribution. solidity σ √ 2π (ln x−µ)2 exp− 2σ 2 where x is 1/solidity and µ and σ are the mean and standard deviation of 1/solidity’s natural logarithm for all connected components on the page. • Log-normal distribution of 1/elongation 2 . Except for some characters such as 1,l and I that resemble a very small rule line, the height and width ratio of a character, namely elongation, is located roughly in a 1 LogNormalDist(1/solidity) 2 LogNormalDist(1/elo) 39
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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
Page 11 and 12: 6.1 PARAGRAPH DETECTION SUCCESS RAT
Page 15 and 16: detection and we conclude that the
Page 21 and 22: Figure 1.8: A screen shot that show
Page 23 and 24: Chapter 2 Related work tel-00912566
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Page 41 and 42: (a) Five zones 1-5 (b) Projection p
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Page 51 and 52: fixed range. When the elongation ap
Page 55 and 56: The second method calculates the co
Page 57 and 58: 3. Repeat for m = 1, 2, ..., M •
Page 61 and 62: Chapter 4 Region detection tel-0091
Page 63 and 64: The next advantage of using CRFs is
Page 65 and 66: weights that are assigned to edge a
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Page 79 and 80: ∂l λ = ∑ ( ∑y∈Y f k (y s ,
Page 81 and 82: incorrect [100]. Several sufficient
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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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