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

List of Figures
tel-00912566, version 1 - 2 Dec 2013
1.1 A sample of a correctly segmented document image by ABBYY
FineReader 2011. . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Left; A document page that is segmented incorrectly by ABBYY
FineReader 2011 and Right; The same document segmented by
our method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Left; A document page that is segmented incorrectly by AB-
BYY Fine Reader 2011 and Right; Its corresponding result for
character recognition. . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 The result of paragraph detection of the image in figure 1.3 by
our method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Some sample documents from our own corpus. . . . . . . . . . . 9
1.6 Two sample documents from the dataset for ICDAR2009 competition
[6]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.7 A screen shot of our developed software for generating XML
ground truth documents for each document image . . . . . . . . 10
1.8 A screen shot that shows part of the contents of an XML file
created by our software . . . . . . . . . . . . . . . . . . . . . . . 11
1.9 Visual organization of this dissertation . . . . . . . . . . . . . . . 12
2.1 Text components touch graphical elements . . . . . . . . . . . . . 15
2.2 Broken components of graphical drawings . . . . . . . . . . . . . 15
2.3 Variability of font sizes in newspaper style documents . . . . . . 22
2.4 Large gaps between words . . . . . . . . . . . . . . . . . . . . . . 23
2.5 Closely situalted text lines . . . . . . . . . . . . . . . . . . . . . . 23
2.6 Skewed text lines in camera-captured documents. . . . . . . . . 24
2.7 Vertical Text lines . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.8 Snakes grow to engulf text lines . . . . . . . . . . . . . . . . . . . 26
2.9 Text line segmentation using global projection profiles . . . . . . 28
2.10 Adaptive local connectivity map for detecting text lines . . . . . 28
2.11 Block extraction steps in [105] . . . . . . . . . . . . . . . . . . . . 29
2.12 Line segmentation using piecewise projection profiles . . . . . . . 30
2.13 Steps for locating text line separators in part of document image.
[75] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.1 Advantages of Sauvola’s binarization method . . . . . . . . . . . 37
3.2 Advantages of Otsu’s binarization method . . . . . . . . . . . . . 38
3.3 Histograms of features based on solidity . . . . . . . . . . . . . . 40
3.4 Histograms of features based on elongation . . . . . . . . . . . . 42
3.5 Histograms of features based on height . . . . . . . . . . . . . . . 43
iv