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

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of each ground-truth image. Black and white areas represent text and background regions respectively. Gray areas however represents areas that could be either text or background. In other words we ignore those areas and do not penalize training algorithms for making a misclassification in those regions. In our first attempts to train our 2D CRF model, we used Collin’s Voted Perceptron. The voted perceptron algorithm does not converge to a global minimum. Generally, depending on the learning rate, it starts with a large classification error, and traverses some local minima of the likelihood function until it goes through a cycle. By analyzing the classification results for each iteration, it became clear that in most of the iterations, the algorithm struggles with one row or one column of pixels around the borders of text regions, which occupy most of the training time without gaining any noticeable results for the human eye. Our idea to resolve this situation is to ignore sites that are located on the borders of text regions in our ground-truth images. We label the sites that are not completely located on text or non-text regions as ”don’t care” regions. tel-00912566, version 1 - 2 Dec 2013 The use of ”don’t care” regions has two advantages. The first advantage is that computed errors indicate information for the sites that are visually important for us and more reliably reflect the quality of the segmentation. The second advantage is that the Voted perceptron method performs more smoothly and well behaved during the training. Whenever we process a ”don’t care” site, we simply do not count the error for that site, and we prevent that site to have any influence on the approximate gradient. The drawback is that we still decode their labels and use those labels in the feature functions of the active surrounding sites that demand it. Figure 4.10 illustrates the number of misclassified sites in 400 iterations of training by Collin’s Voted Perceptron with or without considering ”don’t care” sites. The error rates with or without considering ”don’t care” sites are shown in red and blue, respectively. In each iteration of training, a large vector of weights are stored. The dilemma is which weight vector to choose. Figure 4.10 clearly shows that as the number of misclassified text sites starts to decrease, there is an increase in the classification error for sites that are located between text columns. In other words, those sites, located between text columns, should not be labeled with text, but iteration after iteration text labels start to be build-up in those regions. Considering both numbers, it seems reasonable to use the obtained weights around iteration 147 as the trained parameters for our model. Some obtained results are shown in figure 4.11. We have particularly chosen examples that have more errors. These results are showing the output of CRF without any post-processing. Though, in many cases there are holes inside text regions that can be easily corrected in post-processing. In some cases penetration of non-text labels happen (sub-figure e) at the boundaries of text regions. These penetrations do pose a problem if they divide a single text region into two separate regions. It happens because some feature functions, responsible for capturing gap between side notes, have obtained a large weight. Other type of problem that often occurs (sub-figure g) is touching of a small region between side notes and the main text. There is always a trade-off between misclassification in text regions and gaps between side-notes. 72
tel-00912566, version 1 - 2 Dec 2013 Two sample pages from our training dataset along with their ground- Figure 4.9: truth images. 73
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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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Page 31 and 32: [21] is another texture-based metho
Page 33 and 34: Figure 2.4: Part of a document in o
Page 35 and 36: • Degraded quality due to ageing
Page 37 and 38: 2.3.2 Handwritten text line detecti
Page 39 and 40: (a) Divided strips and their projec
Page 41 and 42: (a) Five zones 1-5 (b) Projection p
Page 43 and 44: would be difficult to draw a conclu
Page 45 and 46: 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: incorrect [100]. Several sufficient
Page 91 and 92: Table 4.3: TION COUNT WEIGHTED SUCC
Page 97 and 98: 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
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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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