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

Although it is not mandatory to segment text regions into paragraphs before
passing text lines to optical character recognition modules, it is necessary to
have correct paragraphs for reading order detection. Thus, in this work we also
group text lines into paragraphs after identifying each text region. It is worth
noting that there are more than one possible way to segment a document image
into text regions correctly that depends on the reading order in the ground
truth, but there is only one solution for segmenting text regions into paragraphs.
tel-00912566, version 1 - 2 Dec 2013
Because of the important role page segmentation plays in document layout
analysis, and of its direct effect on the optical character recognition step, it has
been explored deeply for the last four decades by document imaging community
and many algorithms have been proposed in the literature. A comprehensive
overview of these algorithms is provided by Nagy in [65] and Cattoni et al.
in [22]. In this work we review many of these algorithms in the domain of
text/graphics separation, text line and region detection. Then a new system
for page segmentation is proposed that improves the results of segmentation in
areas where problems still exist by current algorithms.
1.3 Overview of the approach
We view a document as a scene of connected components (CCs). Thus, the
first step in our method is an image binarization and extraction of all connected
components. The goal of the system is to find locations of all paragraphs inside
the document image. In order to form paragraphs, we need to detect text lines
correctly. And to do so, we have to detect text regions considering the geometric
alignment of CCs. Text lines in multi-column documents should be separate
from one another and in the case where side notes exist, we have to use the
alignment of the CCs to separate side notes from the main text. All these operations
should be carried out without reading the actual text or understanding
the context of the text.
After binarization, we have a set of connected components that either belong
to text or non-text regions. The next step is to correctly classify each connected
component. Each CC has a set of intrinsic features (height, width, eccentricity,
...) and a set of extrinsic features (features from surrounding area). Using these
features, we train a set of weak classifiers by the help of boosting which allows
us to classify components as text or non-text. In chapter 3 we go deeply into
the details of this method.
At this point, we are interested to segment a document image into regions
of text that have some form of alignment. We consider both sets of textual and
non textual CCs for this task. Note that some non-text CCs such as tables and
rule lines are highly effective in separating regions, so we do not discard non
textual components. In chapter 4 we show how the method effectively makes
the best out of these sets to separate text inside tables.
Having all text elements and their regions, detecting text lines is the next
step. Chapter 2 thoroughly reviews major methods in the literature for text line
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