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

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Contents tel-00912566, version 1 - 2 Dec 2013 1 Introduction 2 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Document page segmentation . . . . . . . . . . . . . . . . . . . . 3 1.3 Overview of the approach . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Contribution of this dissertation . . . . . . . . . . . . . . . . . . 5 1.5 Our datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.6 Organization of this dissertation . . . . . . . . . . . . . . . . . . 11 2 Related work 13 2.1 Text/graphics separation . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.1 Connected component based methods . . . . . . . . . . . 14 2.1.2 Region-based methods . . . . . . . . . . . . . . . . . . . . 16 2.1.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2 Text region detection . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.1 Distance-based methods . . . . . . . . . . . . . . . . . . . 19 2.2.2 Whitespace analysis . . . . . . . . . . . . . . . . . . . . . 19 2.2.3 Texture-based methods . . . . . . . . . . . . . . . . . . . 20 2.2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3 Text line detection . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.1 Printed text line detection . . . . . . . . . . . . . . . . . . 25 2.3.2 Handwritten text line detection . . . . . . . . . . . . . . . 27 2.3.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3 Text/graphics separation 36 3.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.2 Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.3 Feature analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.4 Classifier selection . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.5 LogitBoost for classification . . . . . . . . . . . . . . . . . . . . . 46 3.6 Post processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.7 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4 Region detection 51 4.1 Conditional random fields (CRFs) . . . . . . . . . . . . . . . . . 53 4.1.1 The three basic problems for CRFs . . . . . . . . . . . . . 55 4.2 Feature functions . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.3 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.3.1 Height and width maps . . . . . . . . . . . . . . . . . . . 58 ii
4.3.2 Text components . . . . . . . . . . . . . . . . . . . . . . . 59 4.3.3 Graphical components . . . . . . . . . . . . . . . . . . . . 59 4.3.4 Horizontal and vertical run-lengths . . . . . . . . . . . . . 59 4.3.5 Gabor features . . . . . . . . . . . . . . . . . . . . . . . . 60 4.4 Feature functions . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.5 Label decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.6 Training (parameter/weight estimation) . . . . . . . . . . . . . . 68 4.6.1 Collin’s voted perceptron method . . . . . . . . . . . . . . 69 4.6.2 Loopy belief propagation . . . . . . . . . . . . . . . . . . 70 4.6.3 L-BFGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.7 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.7.1 Post-processing . . . . . . . . . . . . . . . . . . . . . . . . 76 4.8 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . 76 4.8.1 Discussion on parameters . . . . . . . . . . . . . . . . . . 81 4.9 Final Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 tel-00912566, version 1 - 2 Dec 2013 5 Text line detection 87 5.1 Initial text line separators . . . . . . . . . . . . . . . . . . . . . . 87 5.2 Refinement of initial text line separators . . . . . . . . . . . . . . 88 5.3 Connecting separators across vertical zones . . . . . . . . . . . . 93 5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 6 Paragraph detection 100 6.1 Minimum spanning tree (MST) . . . . . . . . . . . . . . . . . . . 101 6.2 Binary partition tree (BPT) . . . . . . . . . . . . . . . . . . . . . 101 6.3 Paragraph features . . . . . . . . . . . . . . . . . . . . . . . . . . 103 6.4 State decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 6.5 Training method . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 6.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 7 Conclusion and future work 110 7.1 Future direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 A Performance evaluation methods 112 A.1 Precision and recall . . . . . . . . . . . . . . . . . . . . . . . . . . 112 A.2 Match counting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 A.3 Scenario driven region correspondence . . . . . . . . . . . . . . . 115 B Implementation and software 116 Bibliography 118 Index 127 iii
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Page 3 and 4: Resumé La segmentation de page est
Page 5: Acknowledgements This work would no
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
Page 27 and 28: them. In such circumstances, it wou
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 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
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3. Repeat for m = 1, 2, ..., M •
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tel-00912566, version 1 - 2 Dec 201
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Chapter 4 Region detection tel-0091
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The next advantage of using CRFs is
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weights that are assigned to edge a
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{ 1 if ys = text and y f 1 (y s , y
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(a) Document (b) Filled text compon
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tel-00912566, version 1 - 2 Dec 201
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f = [y c = 0] × [y tl = 0] f = [y
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(a) Ground-truth (b) y c = 0 tel-00
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∂l λ = ∑ ( ∑y∈Y f k (y s ,
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incorrect [100]. Several sufficient
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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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tel-00912566, version 1 - 2 Dec 201
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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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