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

Chapter 7
Conclusion and future work
tel-00912566, version 1 - 2 Dec 2013
In this thesis, we have provided a new framework for document page
segmentation that brings some improvements upon current state-ofthe-art
methods.
Unlike most methods that apply page segmentation on all components of the
page, we have presented a method to identify non-textual components and clear
them from the document image before applying a page segmentation method.
Moreover, the identified non-textual components such as rulers, table structures
and advertisement frames are utilized again to separate text regions.
For text region detection, we have introduced a framework based on twodimensional
conditional random fields that can gather various observations from
whitespace analysis, graphical components, run-lengths and separate regions of
text according to these observations. As a result, we were able to improve the
results of separating text columns when one is situated very close to the other.
This framework also prevents the contents of a cell in a table to be merged with
the contents of other adjacent cells. Furthermore, the method does not allow
text regions inside a frame to be merged with other text regions around.
After carefully examining methods for text line detection that can sustain
the variety and variation of text lines in handwritten and printed document,
we have adopted a variant of Papavassiliou’s text line detection method [75] to
segment text regions into text lines.
Finally, a novel trainable method based on binary partition tree is proposed
to determine the paragraph structures based on the appearances of text lines in
a text region.
7.1 Future direction
As with every new work, there are many problems and challenges that can be
overcome with an improvement to the methods presented in this thesis. We
propose several ideas which would clearly be of benefit to this work. We are
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