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

page’s width
log
component’s width
They are normalized for the reason that document pages of our training
dataset come in different resolutions and sizes.
• Logarithm of 1/solidity 8 and 1/elongation 9 . The importance of
solidity and elongation is noted above. Instead of computing the mean
or standard deviation of this feature for components of a page, these two
features are provided as raw and they allow the classifier to compare these
values globally for all images of the training dataset.
tel-00912566, version 1 - 2 Dec 2013
• Logarithm of Hu-moments of the component’s pixels 10 . Image
moments are particular weighted average of the image pixels’ intensities.
They usually are chosen to have some attractive properties or interpretation.
Hu moments are special moments that are proved to be invariant to
the image scale, rotation and reflection. There are a total of seven popular
moments in the literature. Of these moments, the seventh one is not
invariant to reflection. Empirically we found that the first four moments
improve the classification results.
• Parents, children and siblings. Usually text characters that appear on
a page are not contained or do not contain any other components except
when they belong to a table. On the other hand large drawings often
contain many broken pieces. If a component contain another component,
the former is assigned the role of a parent and the latter is assigned the
role of a child. Any component that has a parent might have some other
siblings. By counting the number of parents, children and siblings they
serve as perfect features for our purpose.
3.3 Feature analysis
Having some features, it would make sense to know the contribution that each
feature may bring to the task of classification. Moreover, if a feature has no correlation
with the true labeling of the data, it may do more harm than good and
thus should be pruned. To do so, we apply some feature selection methods to our
dataset and their true labels and each method assigns a weight to each feature
which indicates the significance of the feature based on the method. All parts
of this analysis is carried out by an open-source software called RapidMiner [63]
and for this purpose we use the dataset from ICDAR2009 page segmentation
competition for the reason that it contains a good amount of irregular graphical
components and tables.
Tabled 3.1 summaries the result of feature analysis by showing the obtained
weights for four feature analysis methods. The first method calculates the relevance
of a feature by computing the information gain in class distribution.
8 log(1/solidity)
9 log(1/elo)
10 log(HuMomentX+1)
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