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

Chapter 4
Region detection
tel-00912566, version 1 - 2 Dec 2013
After obtaining two separate images, one containing textual components
and the other containing graphical components, we apply a region detection
method to separate text regions. It is already mentioned that
many authors do not separate text and graphics. They directly apply
their methods to segment regions of text and the decision to assign which region
is text and which is graphics, comes after segmentation. As mentioned before,
when parts of a graphical drawing are located close to text characters and are
aligned with them, they may be merged incorrectly as part of the text regions.
Other authors do apply a text and graphics separation, and then overlook all
graphical components and apply text line detection or region detection only on
textual components. Our method incorporates both textual components as well
as graphics. The method is designed to take advantages of graphical components
such as rule lines to separate text regions.
Although in many situations, rule lines may be helpful to separate text regions
or columns of text, there are many examples where rulers are simply not
available. Many authors have overcome this problem by using a distance-based
approach or analysis of white space with success. However, still the challenge
exists when columns of text are located very close to one another. One hard
case of such an example is to separate side notes from the main text body. The
problem is that in most documents from our corpus, side notes appear so close
to the main text that a distance-based method alone, fails to separate them.
We propose a framework based on two-dimensional Conditional random fields
(CRFs) to separate regions of text from one another that also aims to separate
side notes and text strings inside a table structure.
The first motivation to use CRFs compared to other locally trained machine
learning methods is the long-distance communication between sites in different
parts of the image. In figure 4.1 three regions are shown in red. By only considering
the local information around each of these regions separately, it would
be difficult to recognize these regions as gaps between side notes and the main
text. However, by taking advantage of the CRFs’ long-distance message passing
between these regions and considering the alignment of same-labelled regions,
column separators can be easily detected.
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