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

2.1.1 Connected component based methods
As the name suggests, connected component based methods work with connected
components to discriminate text from graphical elements within the
document image. Maybe one of the earliest methods and still popular for its robustness
and usability with increasingly complex documents is that of Fletcher
and Kasturi [33]. The method is based on Hough transform, working on the
center of the bounding boxes and works by grouping aligned components into
strings of characters. Then it classifies all isolated components as graphics.
There are major drawbacks to this approach:
• Tables and borders around advertisements have a center that is usually
located inside the text area. So it is easy to group them incorrectly as
part of a character chain, unless there are some constraints that govern
the size of component.
tel-00912566, version 1 - 2 Dec 2013
• The classifications of short strings of characters are not reliable due to
lack of votes in the Hough space to efficiently discriminate them.
• The method may find diagonal alignments when text lines are packed
closely and there are not enough gaps between them.
• Punctuation marks, diacritics and broken characters, are not aligned with
other components in a string of text, and they may become a seed for
misclassification.
Despite all these limitations, authors of [96] have recently published a paper
and the results are improved. The difficulty of the problem lies not only in
the classification of these components but also in the separation of interacting
components. When textual and non textual elements interact locally, finding a
solution becomes more difficult. Figure 2.1 shows two cases of such a problem.
In [28] Doermann tries to address this issue with a method based on stroke level
properties to separate components. As an illustration, of the potential discrimination
power of the stroke level properties, it is noted that in hand-completed
forms and pre-printed boxes, lines are produced by a machine and have more
regularity than the associated handwritten text. Considering only the widths
of the strokes and examining the population of widths at the cross section level,
strong separability can be achieved between the two populations.
Another type of problem that frequently arise in a connected component
based method is that large graphical components are often broken into pieces,
and they are composed of many small isolated components that behave like text
components. Many methods try to address this problem by isolating a graphical
component and its sub elements as a whole rather than classifying each one
separately. Figure 2.2 illustrates graphical elements from two documents in our
corpus that exhibit this issue.
One possible solution is to apply a method like the one proposed by B. Waked
in his thesis [99]. The idea is that text regions can be regarded as a set of small
bounding boxes that are regular in height and are usually aligned horizontally
or vertically, whereas a non-text image or half-tone graphics is irregular. The
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