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

Algorithm 2 Calculating delta
procedure CalcDelta(∆)
⊲ ∆ should be initialized with zero.
//* refers to ground truth
if state* = ”Preserve” or this paragraph has no children then
F gt ← F preserve
else
F gt ← F remove
end if
∆ ← ∆ + F gt − F
if this paragraph has children then
First child → CalcDelta(∆)
Second child → CalcDelta(∆)
end if
end procedure
tel-00912566, version 1 - 2 Dec 2013
Table 6.1: PARAGRAPH DETECTION SUCCESS RATES FOR 61 DOCUMENTS
OF ICDAR2009 DATASET
Area weighted error % Area weighted % Count weighted %
Method Merge Split Miss Partial Miss False Detection Overall Overall
Our Method 29.45 22.92 2.84 04.31 23.94 75.55 59.82
Tesseract 14.83 35.35 1.78 03.08 11.53 73.22 56.95
EPITA 13.46 02.45 0.22 05.27 53.27 73.65 59.48
Table 6.2: PARAGRAPH DETECTION SUCCESS RATES FOR 100 DOCU-
MENTS OF ICDAR2011 DATASET
Area weighted error % Area weighted % Count weighted %
Method Merge Split Miss Partial Miss False Detection Overall Overall
Our Method 32.31 20.11 0.02 1.38 04.16 82.22 66.59
Tesseract 29.62 49.24 0.83 0.98 16.07 72.00 52.28
EPITA 30.30 17.46 1.09 6.40 16.24 81.72 61.06
For the purpose of comparison, figure 6.4 shows the results of paragraph
detection on two documents from our corpus using our method and Tesseract-
OCR. These documents are selected in a way that the ratio of overall success
for our method divide by the ratio of overall success for Tesseract-OCR is maximized.
In other words, our method performs way better on these two images
compared to Tesseract-OCR.
Results indicate that Tesseract fails at processing indentations, side notes
detection and also it fails to make use of rule lines and other clues such as borders
and frames to correctly segment the document.
On the other side, figure 6.5 shows the results of paragraph detection on two
other documents from our corpus. This time for the selection of documents, the
mentioned success ratio is minimum. For the images in sub-figures a and b, the
overall area weighted success rate for Tesseract is 98.8% but that of our method
is 79.9%. In the second sub-figures c and d, our method misses many dots that
belong to text lines and due to this problem, the success rate is lower than that
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