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

4.7.1 Post-processing
Three post-processing steps are applied to the output of the CRF:
• Removing regions whose width or height are smaller than the width of
height of the average character.
• Opening each regions separately from other regions. Since side notes are
very close to main text body, care should be taken not to merge two text
regions together.
• Applying a hole-filling method to fill holes inside each text region separately.
tel-00912566, version 1 - 2 Dec 2013
Four pages are shown in figure 4.12 after applying these three post-processing
steps.
4.8 Results and discussion
At this point, regions of text are detected and ready for text line detection,
but paragraphs are yet to be found. Paragraphs may or may not be separated
depending on the distance between them. However since in most ground-truth
data for competitions such as ICDAR2011 Historical Document Layout Competition,
paragraphs are annotated separately, evaluation of the results based
on region matching with the true ground-truth data are meaningless for the
purpose of comparison.
We report the current success rate for site-wise classification. The statistics
aim to show how far the results are from the closest acceptable region segmentation
for the means of text line detection. This means that instead of preparing
a ground-truth that separates all the paragraphs, we generate the ground-truth
data by correcting the segmentation results to make them acceptable for text
line detection.
Table 4.1 indicates number of misclassified sites from the output of our CRF
model.
Table 4.1: NUMBER OF MISCLASSIFIED SITES (%) FROM THE OUTPUT OF
OUR CRF MODEL
Total sites (%) Textual sites (%) Non-textual sites (%) Gap between columns (%)
0.97 1.32 0.88 3.5
Two other tables 4.2 and 4.3 show region segmentation success rates for
different images. The indicated rates are computed between the segmentation
output and the closest acceptable segmentation for text line detection. The
closest acceptable segmentation is a segmentation that is capable of producing
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