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

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same location of i th zone in sub-figure (a) and (b), respectively. where the weights are: SP R i = w j = M∑ j=−M w j P R i+j . exp −3|j| M+1 ∑ M −3|k| k=−M exp M+1 Weights are defined to decay exponentially with respect to the distance from the zone in the middle. Finally, the first derivative of a smoothed projection may be obtained using a symmetric difference equation as follows: tel-00912566, version 1 - 2 Dec 2013 2 2h∑ △SP R i (j) = h( h 2 + 1) k. (SP R i (j + k) − SP R i (j − k)) . k=1 where h is set to the closest odd integer value to the mean height of all CCs. The upper and lower bounds of text lines are the local maxima and local minima of △SP R i respectively. Likewise gap regions are identified as the areas between consecutive minima and maxima of △SP R i . Having the first derivatives of the smoothed projection profiles, finding the initial text and gap regions is a matter of applying a threshold to these first derivatives. Figure 5.3 shows the initial text and gap regions for the two image in figures 5.1 and 5.2. Initial separators for separating text lines can be drawn in the middle of each gap in each vertical zone. In this regard, separators are line segments that are defined in the middle of each gap region inside every vertical zone. They are used later to find text lines. 5.2 Refinement of initial text line separators Initial text line separators contain two types of errors: redundant separators resulting from misclassified text and gaps due to poor local extrema, introduced in the smoothed derivatives and separators that cut through descenders or ascenders of text characters. In order to correct these errors and to locate separators more accurately, a Hidden Markov Model (HMM) [78, 91] is formulated with parameters drawn from statistics of the initial text and gap regions. For each document, an HMM is formulated separately on-the-fly and is applied once to each vertical strip of text and gap regions. Viterbi decoding scheme [34] is applied on each zone to obtain a better succession of text and gaps. We briefly note some formal definitions of HMM. Our HMM is characterized by the following items: 88
tel-00912566, version 1 - 2 Dec 2013 (a) Binary image (b) Vertical projection profiles (c) Smoothed projection profiles (d) First derivatives of smoothed profiles Figure 5.1: First steps in line detection to obtain initial lines and gaps for a single document image. 89
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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],
Page 47 and 48: tel-00912566, version 1 - 2 Dec 201
Page 49 and 50: is assigning a label to a region of
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
Page 67 and 68: { 1 if ys = text and y f 1 (y s , y
Page 69 and 70: (a) Document (b) Filled text compon
Page 75 and 76: f = [y c = 0] × [y tl = 0] f = [y
Page 77 and 78: (a) Ground-truth (b) y c = 0 tel-00
Page 79 and 80: ∂l λ = ∑ ( ∑y∈Y f k (y s ,
Page 81 and 82: incorrect [100]. Several sufficient
Page 91 and 92: Table 4.3: TION COUNT WEIGHTED SUCC
Page 97: Chapter 5 Text line detection tel-0
Page 103 and 104: Having specified the model, a verti
Page 105 and 106: • The fifth step is to remove ext
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 and 130: [12] T. M. Breuel. Two geometric al
Page 131 and 132: [39] B. Gatos, A. Antonacopoulos, a
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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