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

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

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14.01.2014 Views

2.1.1 Connected component based methods As the name suggests, connected component based methods work with connected components to discriminate text from graphical elements within the document image. Maybe one of the earliest methods and still popular for its robustness and usability with increasingly complex documents is that of Fletcher and Kasturi [33]. The method is based on Hough transform, working on the center of the bounding boxes and works by grouping aligned components into strings of characters. Then it classifies all isolated components as graphics. There are major drawbacks to this approach: • Tables and borders around advertisements have a center that is usually located inside the text area. So it is easy to group them incorrectly as part of a character chain, unless there are some constraints that govern the size of component. tel-00912566, version 1 - 2 Dec 2013 • The classifications of short strings of characters are not reliable due to lack of votes in the Hough space to efficiently discriminate them. • The method may find diagonal alignments when text lines are packed closely and there are not enough gaps between them. • Punctuation marks, diacritics and broken characters, are not aligned with other components in a string of text, and they may become a seed for misclassification. Despite all these limitations, authors of [96] have recently published a paper and the results are improved. The difficulty of the problem lies not only in the classification of these components but also in the separation of interacting components. When textual and non textual elements interact locally, finding a solution becomes more difficult. Figure 2.1 shows two cases of such a problem. In [28] Doermann tries to address this issue with a method based on stroke level properties to separate components. As an illustration, of the potential discrimination power of the stroke level properties, it is noted that in hand-completed forms and pre-printed boxes, lines are produced by a machine and have more regularity than the associated handwritten text. Considering only the widths of the strokes and examining the population of widths at the cross section level, strong separability can be achieved between the two populations. Another type of problem that frequently arise in a connected component based method is that large graphical components are often broken into pieces, and they are composed of many small isolated components that behave like text components. Many methods try to address this problem by isolating a graphical component and its sub elements as a whole rather than classifying each one separately. Figure 2.2 illustrates graphical elements from two documents in our corpus that exhibit this issue. One possible solution is to apply a method like the one proposed by B. Waked in his thesis [99]. The idea is that text regions can be regarded as a set of small bounding boxes that are regular in height and are usually aligned horizontally or vertically, whereas a non-text image or half-tone graphics is irregular. The 14

tel-00912566, version 1 - 2 Dec 2013 Figure 2.1: The image on the Left shows part of a document page from [96] when text and numerical components touch graphical elements and the image on the Right is part of a document from our corpus showing a difficult situation when underlines touch characters of a text line. Figure 2.2: Two graphical figures from our corpus that exhibit broken components with text like features. 15

tel-00912566, version 1 - 2 Dec 2013<br />

Figure 2.1: The image on the Left shows part <strong>of</strong> a <strong>document</strong> page from [96] when<br />

text <strong>an</strong>d numerical components touch graphical elements <strong>an</strong>d the image on the Right<br />

is part <strong>of</strong> a <strong>document</strong> from our corpus showing a difficult situation when underlines<br />

touch characters <strong>of</strong> a text line.<br />

Figure 2.2: Two graphical figures from our corpus that exhibit broken components<br />

with text like features.<br />

15

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