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

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f = [y c = 1] × [y l = 1] × (x c + x l ) The mentioned feature function prototypes are designed empirically with extensive testing. Again, we note that due to a limitation in our inference method, the label of a site only depends on the labels of sites on its left or top; however, observations do not have this restriction. The observation x in these functions can be substitute by the normalized mean and variance of any extracted feature on the previous sections. tel-00912566, version 1 - 2 Dec 2013 There is one issue that deserves an explanation. In these feature functions, we add +1 and -1 to each observation. Note that observations are normalized between 1 and -1. The question is: what are the advantages of using two feature functions for an observation instead of one? To explain this we turn into a toy example. First, we define f 1 which is a feature function that detects non-text areas with a normalized observation. Equivalently, we have f 2 and f 3 . We argue that using f 2 and f 3 instead of f 1 is more effective for the overall feature space. f 1 = [y c = 0] × x f 2 = [y c = 0] × 0.5 × (x + 1) f 3 = [y c = 0] × 0.5 × (x − 1) In figure 4.8, a ground-truth image with 3 × 3 sites is shown. This groundtruth shows that this particular toy example has three textual sites and six non-textual sites. In addition, the extracted observation x is shown. Having said that, all the feature functions f 1 ,f 2 and f 3 only interact with non-textual areas. In other words, their values for text sites are zero. The sum of the values of f 1 for non-textual sites is zero. It means that the values of x for non-textual sites cancel each other and a training algorithm such as voted percepton assigns a zero weight to this feature function. However, the sum of the values of f 2 and f 3 are 3 and -3, respectively. It means that f 2 positively contributes to some sites for having a ”non-text” label and f 3 with a negative sign contributes to some other sites for having a ”non-text” label. Finally, the training algorithm assigns a positive weight to f 2 and a negative weight to f 3 and both feature functions would contribute positively to the overall classification. 4.5 Label decoding Label decoding is one of the crucial steps in training a conditional random field’s model and detecting text regions. For now, we assume that we have a trained model in which every parameter in our model is set according to our training dataset. Given a document image, we divide our image into sites and calculate observation vectors for each site. Label decoding refers to the process of assigning ”text” or ”non-text” labels to each size in such a way that the conditional 66
(a) Ground-truth (b) y c = 0 tel-00912566, version 1 - 2 Dec 2013 (c) x (d) f 1 (e) 0.5 × (x + 1) (f) 0.5 × (x − 1) (g) f 2 (h) f 3 Figure 4.8: A toy example that shows why a normalized observation is better to appear in two feature functions instead of just one. 67
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tel-00912566, version 1 - 2 Dec 201
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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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Page 27 and 28: them. In such circumstances, it wou
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Page 37 and 38: 2.3.2 Handwritten text line detecti
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Page 41 and 42: (a) Five zones 1-5 (b) Projection p
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Page 57 and 58: 3. Repeat for m = 1, 2, ..., M •
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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: f = [y c = 0] × [y tl = 0] f = [y
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Page 91 and 92: Table 4.3: TION COUNT WEIGHTED SUCC
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Page 125 and 126: 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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