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

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tel-00912566, version 1 - 2 Dec 2013 Figure 1.9: A visualization of the structure of this thesis displaying the connection between different chapters and their contribution to different areas of page segmentation. 12
Chapter 2 Related work tel-00912566, version 1 - 2 Dec 2013 2.1 Text/graphics separation Separating text and non-text elements inside a document image is the foremost part of any page segmentation algorithm. In other words, the output of a page segmentation algorithm should not contain graphics, rule-lines or any other element that is not in coherence with OCR/HCR 1 modules. Due to the importance of this step, attempts have been made to develop effective methods and algorithms since the early days of document image analysis. However, the choice of an optimal method depends heavily on the type of the document image that has to be processed. Documents in general can be divided into two major categories [65]. Maps, music scores, engineering drawing and organization charts are samples of the first category which contain mostly graphics. Scanned pages, camera-captured forms, text books, historical manuscripts and advertisements belong to the second category which contains mostly text. In general, obtaining text characters by analysis of connected components is much easier in mostly text documents compared to mostly graphics due to the simplicity of its background. Textured backgrounds are particularly difficult to handle. Examples of such textured backgrounds can be found frequently in camera-captured scenes. One might reason that camera captured scenes are out of the scope of document image analysis, but sometimes methods for locating text in these scenes can be adapted for the field of document analysis as it did in ICDAR2005 text locating competition [59]. For this reason, two groups of algorithms have emerged. The first group of methods work at the connected component level. These methods assign a label text, non-text to each connected component of the image. The methods in the second group analyze the texture of a region containing pixels within an image which can be of convex or free shape and assign one of the mentioned labels to that region. In this section, we briefly note some of these methods and outline their strengths and weaknesses. 1 Handprint character recognition 13
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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 15 and 16: detection and we conclude that the
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Page 27 and 28: them. In such circumstances, it wou
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f = [y c = 0] × [y tl = 0] f = [y
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(a) Ground-truth (b) y c = 0 tel-00
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∂l λ = ∑ ( ∑y∈Y f k (y s ,
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incorrect [100]. Several sufficient
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Table 4.3: TION COUNT WEIGHTED SUCC
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Chapter 5 Text line detection tel-0
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Having specified the model, a verti
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• The fifth step is to remove ext
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text lines can be divided into two
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the two children. The root node rep
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leaves of the tree which contain on
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currently working on some of these
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• fn (false negative) is the numb
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2 ∗ RA ∗ DR F − Measure = RA
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• ”-tn”: This option uses the
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[12] T. M. Breuel. Two geometric al
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[39] B. Gatos, A. Antonacopoulos, a
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[64] K. P. Murphy, Y. Weiss, and M.
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[91] M. Stamp. A revealing introduc
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Index tel-00912566, version 1 - 2 D
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