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

More documents

Recommendations

Info

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 106
tel-00912566, version 1 - 2 Dec 2013 Figure 6.3: Updated results reported in [4] based on scaled estimates. In this figure, the results from our method (Demat) and Tesseract-OCR are added based on scaled estimates by using results of EPITA as a reference. Table 6.3: PARAGRAPH DETECTION SUCCESS RATES FOR 100 DOCU- MENTS OF OUR CORPUS Area weighted error % Area weighted % Count weighted % Method Merge Split Miss Partial Miss False Detection Overall Overall Our Method 23.88 27.08 0.39 3.03 12.23 86.97 72.71 Tesseract 16.11 44.95 0.51 2.38 23.86 81.79 62.55 EPITA 23.08 19.03 0.85 8.82 12.82 88.05 67.84 of the Tessseract-OCR. 107
Page 1 and 2:
tel-00912566, version 1 - 2 Dec 201
Page 3 and 4:
Resumé La segmentation de page est
Page 5 and 6:
Acknowledgements This work would no
Page 7 and 8:
4.3.2 Text components . . . . . . .
Page 9 and 10:
3.6 Two documents that have obtaine
Page 11 and 12:
6.1 PARAGRAPH DETECTION SUCCESS RAT
Page 13 and 14:
tel-00912566, version 1 - 2 Dec 201
Page 15 and 16:
detection and we conclude that the
Page 17 and 18:
tel-00912566, version 1 - 2 Dec 201
Page 19 and 20:
tel-00912566, version 1 - 2 Dec 201
Page 21 and 22:
Figure 1.8: A screen shot that show
Page 23 and 24:
Chapter 2 Related work tel-00912566
Page 25 and 26:
tel-00912566, version 1 - 2 Dec 201
Page 27 and 28:
them. In such circumstances, it wou
Page 29 and 30:
tel-00912566, version 1 - 2 Dec 201
Page 31 and 32:
[21] is another texture-based metho
Page 33 and 34:
Figure 2.4: Part of a document in o
Page 35 and 36:
• Degraded quality due to ageing
Page 37 and 38:
2.3.2 Handwritten text line detecti
Page 39 and 40:
(a) Divided strips and their projec
Page 41 and 42:
(a) Five zones 1-5 (b) Projection p
Page 43 and 44:
would be difficult to draw a conclu
Page 45 and 46:
The proposed methods by Xiao [102],
Page 47 and 48:
tel-00912566, version 1 - 2 Dec 201
Page 49 and 50:
is assigning a label to a region of
Page 51 and 52:
fixed range. When the elongation ap
Page 53 and 54:
tel-00912566, version 1 - 2 Dec 201
Page 55 and 56:
The second method calculates the co
Page 57 and 58:
3. Repeat for m = 1, 2, ..., M •
Page 59 and 60:
tel-00912566, version 1 - 2 Dec 201
Page 61 and 62:
Chapter 4 Region detection tel-0091
Page 63 and 64:
The next advantage of using CRFs is
Page 65 and 66: weights that are assigned to edge a
Page 67 and 68: { 1 if ys = text and y f 1 (y s , y
Page 69 and 70: (a) Document (b) Filled text compon
Page 71 and 72: tel-00912566, version 1 - 2 Dec 201
Page 75 and 76: f = [y c = 0] × [y tl = 0] f = [y
Page 77 and 78: (a) Ground-truth (b) y c = 0 tel-00
Page 79 and 80: ∂l λ = ∑ ( ∑y∈Y f k (y s ,
Page 81 and 82: incorrect [100]. Several sufficient
Page 91 and 92: Table 4.3: TION COUNT WEIGHTED SUCC
Page 97 and 98: Chapter 5 Text line detection tel-0
Page 103 and 104: Having specified the model, a verti
Page 105 and 106: • The fifth step is to remove ext
Page 109 and 110: text lines can be divided into two
Page 111 and 112: the two children. The root node rep
Page 113 and 114: leaves of the tree which contain on
Page 115: tel-00912566, version 1 - 2 Dec 201
Page 121 and 122: currently working on some of these
Page 123 and 124: • fn (false negative) is the numb
Page 125 and 126: 2 ∗ RA ∗ DR F − Measure = RA
Page 127 and 128: • ”-tn”: This option uses the
Page 129 and 130: [12] T. M. Breuel. Two geometric al
Page 131 and 132: [39] B. Gatos, A. Antonacopoulos, a
Page 133 and 134: [64] K. P. Murphy, Y. Weiss, and M.
Page 135 and 136: [91] M. Stamp. A revealing introduc
Page 137 and 138: Index tel-00912566, version 1 - 2 D
show all

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

Create successful ePaper yourself

Delete template?

Save as template?