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

tel-00912566, version 1 - 2 Dec 2013
Figure 3.7: Some of the misclassified components gathered from our own documents.
Black components are labelled correctly. Red components should have been assigned a
graphics label but they are incorrectly labelled as text. Blue components on the other
hand have text label in ground-truth, however they are misclassified as graphics.
A serious challenge in some documents is a problem that arises due to underlines.
Underlines that appear in the middle of a text region as shown in
figure 3.7, pose two problems. These underlines are treated as graphical components
and are removed from the set of text components, but in text region
detection, they are utilized to separate region of text. This behavior is expected
from a true graphical component, but an underline in the middle of a
text region may split the region into two which is an understandable side effect
in this situation. Moreover, in some situations where text characters are attached
to the underline, not only the underline disappear from the text region,
it takes some characters with it and leaves large gaps in the middle of a text
region. This has a negative effect on our region detection stage when it happens.
Here is another comparison between the results of the method , described
here and the results of text and graphics separation from Tesseract-OCR and
EPITA methods. The classifier for our method is trained on 26 documents,
selected from both ICDAR2011 and our corpus datasets. Tables 3.4,3.5 and 3.6
show the results.
In conclusion, this chapter provides a method for separating text/graphics
components with good separation accuracy.
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