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

tel-00912566, version 1 - 2 Dec 2013
Algorithm 1 State decoding
procedure DecodeStates()
F preserve ← feature vector from current paragraph
if this paragraph has no children then
state ← ”Preserve”
F ← F preserve
else
First child → DecodeStates()
F 1 ← feature vector from first child
Second child → DecodeStates()
F 2 ← feature vector from second child
F Remove ← F 1 + F 2
C preserve ← W T F preserve ⊲ Cost of preserving this paragraph
C remove ← W T F remove
⊲ Cost of removing this paragraph
if C preserve < C remove then
state ← ”Preserve”
F ← F preserve
else
state ← ”Remove”
F ← F remove
end if
end if
end procedure
6.6 Results
We apply our paragraph detection method on three datasets. These datasets
include 55 documents from ICDAR2009 page segmentation competition [6], 100
documents from ICDAR2011 historical document layout analysis competition
[4] and 100 documents from our own corpus. We also apply the Tesseract-OCR
[89] and EPITA [4] page segmentation methods on all datasets for the purpose
of comparison.
The evaluations are carried out by Prima Layout Evaluation Tool [25]. The
full description of the evaluation method is noted in appendix A. Tables 6.1,
6.2 and 6.3 summarize the results. In the reported percentages, area weighted
errors and success rates are more important, because they also account for the
area that a violation happens.
Results show that all methods perform roughly the same on well formatted
magazines and journals from ICDAR2009 dataset with our method doing
slightly better.
On the other hand, on historical documents of ICDAR2011 competition
dataset, our method and the EPITA perform significantly better than Tesseract-
OCR with our method performing slightly better. According to this result, the
reported results in [4] is updated in figure 6.3 computed based on scaled estimates.
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