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

(a) Divided strips and their projection profiles
(b) Extracted blocks
Figure 2.11: Block extraction steps in [105]
tel-00912566, version 1 - 2 Dec 2013
method works on blocks. But the difference is that the authors assume two different
types of documents, namely tightly spaced documents (TSD) and widely
spaced documents (WSD) to better cope with overlapping and multi-touching
components. Figure 2.11 shows the projection profiles for several strips and
the result of their block extraction. For each block, the method computes two
features based on fractal dimensions resulting from the classical box-counting
algorithm. Then an unsupervised fuzzy C-means 2-class classifier is used to
separate blocks into tightly packed or widely spaced. Each block type is approached
differently for detecting text lines.
Another method is proposed by Arivazhagan et al. in [7] that starts by
obtaining candidate lines using piecewise vertical projection profiles similar to
the one used in [105]. Then the method draws a decision for any obstructing
element based on the bivariate Gaussian probability density of a distance metric
to find out whether the element belongs to the line above or the line below.
After applying the piecewise projection profile to the document image, the complete
set of text line separators are drawn by connecting a valley of a profile
associated with a block on the right, to a valley from the block on its left and
continuing the line straightly in a situation where a valley is still unconnected.
For each connected component in which a line passes through, it may belong
totally to either the line above or the live below, or it may need to be broken
into two components. The method uses a distance metric decision to determine
which approach should be taken for each obstructing component.
The last reviewed projection based method is proposed by Papavassiliou et
al. [75]. This method is very similar to the one in [7] with one difference that
it adds another stage based on Hidden Markov Models (HMM) to correct some
misleading peaks and valleys of projection profiles and therefore, it segments
vertical strips into better situated blocks compared to other methods. Initially,
like other methods, text and gap areas are extracted by detecting peaks and
valleys in a smoothed projection profile computed for each vertical strip. Then
a Viterbi algorithm locates the optimal succession of text and gap areas based
upon the statistics drawn from the initial set of blocks. Finally, a text line
separating technique is applied to assign connected components into appropriate
text lines. Results for preliminary version of this method are published in
[90] and submitted to the ICDAR2007 [38] and ICDAR2009 [40] handwritten
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