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

3.6 Two documents that have obtained the lowest accuracy rate for
text/graphics separation. . . . . . . . . . . . . . . . . . . . . . . 48
3.7 Misclassified components, gathered from our own dataset . . . . 49
tel-00912566, version 1 - 2 Dec 2013
4.1 Long-distance communication between image sites in CRFs. . . . 52
4.2 Our two-dimensional conditionl random fields model . . . . . . . 54
4.3 Height and width maps . . . . . . . . . . . . . . . . . . . . . . . 58
4.4 A document image, its filled text and graphical components, separated
using our text/graphics separation method. . . . . . . . . 59
4.5 Horizontal, vertical and marginal run-length maps as features . . 61
4.6 Results of applying different Gabor filters to a document image. . 63
4.7 Comparison between Gabor filters with different kernel sizes . . . 64
4.8 A toy example that shows why a normalized observation is better
to appear in two feature functions instead of just one. . . . . . . 67
4.9 Two sample pages from our training dataset along with their
ground-truth images. . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.10 Number of misclassified sites per iteration using voted perceptron
training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.10 Number of misclassified sites per iteration using voted perceptron
training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.11 Several obtained results for text region detection without postprocessing.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.11 Several obtained results for text region detection without postprocessing.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.12 Results of text region detection after post-processing. . . . . . . . 79
4.13 Non-textual holes and penetrations . . . . . . . . . . . . . . . . . 82
4.14 Title breakdown . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.15 Slanted text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.16 Learning rate parameter . . . . . . . . . . . . . . . . . . . . . . . 84
4.17 Overlapping ratio parameter . . . . . . . . . . . . . . . . . . . . . 85
4.18 Maximum number of ICM cycles . . . . . . . . . . . . . . . . . . 86
5.1 First steps in line detection to obtain initial lines and gaps for a
single document image. . . . . . . . . . . . . . . . . . . . . . . . 89
5.2 First steps in line detection to obtain initial lines and gaps for a
single document image. . . . . . . . . . . . . . . . . . . . . . . . 90
5.3 Initial text and gap regions . . . . . . . . . . . . . . . . . . . . . 91
5.4 Refined text and gap regions . . . . . . . . . . . . . . . . . . . . 94
5.5 Some steps to get from separators to text lines . . . . . . . . . . 96
5.5 Some steps to get from separators to text lines . . . . . . . . . . 97
6.1 A fictional text region including its text lines . . . . . . . . . . . 102
6.2 Binary partition tree generated from couple of text lines. . . . . . 102
6.3 Updated results for ICDAR2011 competition . . . . . . . . . . . 107
6.4 Paragrah detection results (1) . . . . . . . . . . . . . . . . . . . . 108
6.5 Paragrah detection results (2) . . . . . . . . . . . . . . . . . . . . 109
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