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In evolutionary biology, for example, they can be used to detect character associations [ 1- 3].
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To detect characters, a character detection algorithm searches for objects with similar heights and areas.
Finally, text strings are formed with a graph model on detected characters.
Text-oriented color reduction method proposed by Nikolaou and Papamarkos [14] and applied in [9] successfully deals with the problem of partially detected characters (see Figure 2c).
According to the radar dynamic detecting characters during the whole penetration course, a dynamic stealth performance evaluating model is proposed accompanied by a series of stealth ability estimation rules.
To overcome the problems of partially detected characters and lost text colors, we propose the following strategy (see Figure 3): we expand the Nikolaou text-oriented color reduction method [14] with additional stages, i.e., SWT filter and SWT direction determination, and upgrade the mean-shift stage with SWT voting.
In other words, our ability to detect true character correlations in evolutionary history may be compromised.
SWT fails to detect whole characters.
Unfortunately, it often fails to detect whole characters since strokes are not necessarily completely parallel (see Figure 2b).
However, it often fails to detect whole characters and leaves parts of them undetected (see letter 'A' in 'RHODIA' in Figure 2b).
Pairwise distances were computed using the Kimura 2-parameter model in MEGA 4.0 [67].We used the sequence editor Se-Al v2.0a11 (http://tree.bio.ed.ac.uk/software/seal/) to detect diagnostic characters.
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