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

size of its components varies from small to large, and they are rarely aligned
in either direction. Therefore, the sum projection of pixels in non-text regions
on x or y axis does not present a regularity between the peaks and the gaps.
The method uses the standard deviation of runs in projection profiles to decide
whether the region contains text or graphics. At this point, the reader might
wonder ”where do these regions come from?” As it turned out the method,
first performs region detection by analyzing diagonal run-lengths around each
connected component, so as to first detect regions before taking out graphical
elements. Because of this, if the region detection merges a text region with a
closely located non-textual element, the result from the method is unpredictable.
tel-00912566, version 1 - 2 Dec 2013
Another approach proposed by Bloomberg [10] is solely based on image processing
techniques and has been recently revisited by [15]. The main technique
in Bloomberg’s text/graphics separation method is based on multi-resolution
morphology. In this method, an image is closed with a rather large structure element
to consolidate half-tone or broken components. Then the image is opened
with an even larger structure element to remove text blobs (strings of characters)
and to preserve some portions of graphical components. The remaining
portions, called seeds, can be grown to cover graphics. In [15] Bukhari explains
that if a non-text component is only composed of thin lines and small drawings,
then it vanishes from the seed image after the second opening with the large
structure element. Therefore, Bloomberg’s method often fails to detect nontextual
components when they do not contain any solid bunch of pixels. Then,
Bukhari proposes several modifications to the original algorithm that improves
the classification accuracy by a 3% and 6% increase on UW-III and ICDAR2000
datasets, respectively. One of the modifications is to add a hole-filling morphological
operation after the first closing. While the hole-filling step improves the
detection of non-text elements, it cannot be profitable for the documents in our
corpus because of the many document images that contain advertisements with
large frames around a bunch of text lines. The hole-filling step, fills inside these
frames and the page becomes empty of text. The same fate happens to table
structures with closed boundaries.
A rather different approach is utilized in [16] and [42]. Both methods extract
features from connected components and use some form of machine learning to
train a classifier. The first method extracts the shape of a component and
its surrounding area. Then a multi-layer perceptron (MLP) neural network is
trained to learn these shapes and classify the component as either text or nontext.
The second method extracts a set of 17 geometrical features (e.g. aspect
ratio, area ratio, compactness, number of holes) that are computed from the
component, and a support vector machine (SVM) is trained to handle classification.
2.1.2 Region-based methods
Not all methods are applicable to documents with complex background structure.
In fact, in some cases it is not even feasible to extract text components
because different shades of colors pass over and through the text region and
generate many components in such a way that text components are lost among
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