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

would be difficult to draw a conclusion solely based on the reported accuracy
rate of each method. Also it is impossible to implement or find every method
that is listed here. As a consequence we have to rely as much as possible on
the reported accuracies when comparison is possible and then a theoretical and
fundamental reasoning to decide which method is fit for our work. Table 2.1
shows some of the characteristics of our dataset.
Table 2.1:
CHARACTERISTICS OF THE DOCUMENTS IN OUR CORPUS
tel-00912566, version 1 - 2 Dec 2013
Frequently occurs Seldom occurs Never occurs
Printed text lines Slanted text lines Calligraphy
Handwritten text lines Vertical text lines Highly skewed text lines
Large gaps between words Degraded quality
Rule lines
Transparency effect
Close text lines
Multi-script documents
Touching text lines Black borders
Multi-column documents
Salt & Paper noise
Side notes
Variety of font sizes
In order to choose the best line detection method as the base for our work,
first we have to compare methods in a situation where both datasets and the
evaluation metrics are the same, then after pruning weak methods, we have
to go into details of remaining methods to reason whether they might fail to
achieve good results when applied to our documents or not.
We have named fourteen methods for detection of text lines so far. Table 2.2
presents some of these methods, and the accuracy rate that they have achieved
by applying to ICDAR2007 [39] dataset.
Table 2.2: ACCURACY RATES FOR METHODS APPLIED TO ICDAR2007
DATASET [39]
Method Reported In dataset Evaluation Method Accuracy
Papavassiliou 2010 [75] [75] ICDAR07 Match Counting 98.3%
Louloudis 2009 [58] [58] ICDAR07 Match Counting 97.4%
Stafylakis 2008[90] [90] ICDAR07 Match Counting 97.1%
Louloudis 2006 [56] [39] ICDAR07 Match Counting 95.4%
Bukhari 2009a [18] [18] ICDAR07 Match Counting 90.7%
Another table 2.3 presents the detection accuracy rate for CBDAR2007
dataset. Note that for the two methods reported there, the comparison is a
challenge because one method gains the upper hand using one evaluation metric
and using another evaluation metric the situation changes. To solve this
dilemma, we decide to choose the evaluation metric that is more appropriate
and well designed. In this case, it is Match Counting, which is the same evaluation
that we have described in Appendix A. Based on this evaluation metric
the result of both methods can be considered the same.
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