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

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List <strong>of</strong> Tables tel-00912566, version 1 - 2 Dec 2013 2.1 CHARACTERISTICS OF THE DOCUMENTS IN OUR CORPUS 33 2.2 ACCURACY RATES FOR METHODS APPLIED TO ICDAR2007 DATASET [39] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.3 ACCURACY RATES FOR METHODS APPLIED TO CBDAR2007 DATASET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.4 ACCURACY RATES FOR METHODS APPLIED TO UNIVER- SITY OF MARYLAND’S DATASET . . . . . . . . . . . . . . . . 34 2.5 ACCURACY RATES FOR ALL REMAINING METHODS . . . 34 3.1 ANALYSIS OF FEATURES FOR TEXT GRAPHICS SEPARA- TION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 AVERAGE PERFORMANCE OF SEVERAL CLASSIFIERS FOR TEXT GRAPHICS SEPARATION . . . . . . . . . . . . . . . . . 46 3.3 EVALUATION OF LOGITBOOST ON TWO DATASETS FOR TEXT/GRAPHICS SEPARATION BEFORE POST-PROCESSING 47 3.4 COMPARISON OF TEXT/GRAPHICS SEPARATION WITH LOGITBOOST, TESSERACT-OCR AND EPITA ON ICDAR2009 (61 DOCUMENTS) . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.5 COMPARISON OF TEXT/GRAPHICS SEPARATION WITH LOGITBOOST, TESSERACT-OCR AND EPITA ON ICDAR2011 (100 DOCUMENTS) . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.6 COMPARISON OF TEXT/GRAPHICS SEPARATION WITH LOGITBOOST, TESSERACT-OCR AND EPITA ON OUR COR- PUS (97 DOCUMENTS) . . . . . . . . . . . . . . . . . . . . . . 50 4.1 NUMBER OF MISCLASSIFIED SITES (%) FROM THE OUT- PUT OF OUR CRF MODEL . . . . . . . . . . . . . . . . . . . . 76 4.2 AREA WEIGHTED SUCCESS RATES FOR REGION SEG- MENTATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.3 COUNT WEIGHTED SUCCESS RATES FOR REGION SEG- MENTATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.1 LINE DETECTION SUCCESS RATES FOR 61 DOCUMENTS OF ICDAR2009 DATASET . . . . . . . . . . . . . . . . . . . . . 98 5.2 LINE DETECTION SUCCESS RATES FOR 100 DOCUMENTS OF ICDAR2011 DATASET . . . . . . . . . . . . . . . . . . . . . 98 5.3 LINE DETECTION SUCCESS RATES FOR 100 DOCUMENTS OF OUR CORPUS . . . . . . . . . . . . . . . . . . . . . . . . . . 98 vi
6.1 PARAGRAPH DETECTION SUCCESS RATES FOR 61 DOC- UMENTS OF ICDAR2009 DATASET . . . . . . . . . . . . . . . 106 6.2 PARAGRAPH DETECTION SUCCESS RATES FOR 100 DOC- UMENTS OF ICDAR2011 DATASET . . . . . . . . . . . . . . . 106 6.3 PARAGRAPH DETECTION SUCCESS RATES FOR 100 DOC- UMENTS OF OUR CORPUS . . . . . . . . . . . . . . . . . . . . 107 tel-00912566, version 1 - 2 Dec 2013 1
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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: 3.6 Two documents that have obtaine
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
Page 57 and 58: 3. Repeat for m = 1, 2, ..., M •
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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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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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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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