Many documents contain (free-hand) underlining, "COPY" stamps, crossed out text, doodling and other "clutter" that
occlude the text. In many cases, it is not possible to separate the text from the clutter. Commercial OCR solutions
typically fail for cluttered text. We present a new method for finding the clutter using path analysis of points on the
skeleton of the clutter/text connected component. This method can separate the clutter from the text even for fairly
complex clutter shapes.
Even with good localization of occluding clutter, it is difficult to use feature-based recognition for occluded characters,
simply because the clutter affects the features in various ways. We propose a new algorithm that uses adapted templates
of the font in the document that can be used for all forms of occlusion of the character. The method finds the simulated
localization of the corresponding clutter in the templates and compares the unaffected parts of the templates and the
character. The method has proved highly successful even when much of the character is occluded. We present examples
of clutter localization and character recognition with occluded characters.
A model for calculating the spectral radiation difference (contrast) between an object and a sea background has been developed at the Norwegian Defence Research Establishment (NDRE). The model calculates both the emitted radiance from the sea surface and the reflected sky radiance. Inclusion of a rough sea surface is also possible. The contrast has been calculated for different meteorological situations ranging from a cold, clear winter atmosphere to an overcast tropical. A comparison between the contrast in the midwave (3 - 5 micrometers ) and the longwave (7 - 14 micrometers ) band has been made.
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