| Techreport |
| Type of Publication |
| Robust Character Recognition in Low-Resolution Images and
Videos |
| Title |
|
|
| Authors |
| Technical Report TR-05-002, Department for Mathematics and Computer Science,
University of Mannheim, April 2005 |
| Published in |
| Although OCR techniques work very reliably
for high-resolution documents, the recognition of superimposed
text in low-resolution images or videos with a complex background
is still a challenge. Three major parts characterize our system
for recognition of superimposed text in images and videos:
localization of text regions, segmentation (binarization) of
characters, and recognition. We use standard approaches to locate
text regions and focus in this paper on the last two steps. Many
approaches (e.g., projection pro- files, k-mean clustering) do
not work very well for separating characters with very small font
sizes. We apply in a vertical direction a shortest-path algorithm
to separate the characters in a text line. The recognition of
characters is based on the curvature scale space (CSS) approach
which smoothes the contour of a character with a Gaussian kernel
and tracks its inflection points. A major drawback of the CSS
method is its poor representation of convex segments: Convex
objects cannot be represented at all due to missing inflection
points. We have extended the CSS approach to generate feature
points for concave and convex segments of a contour. This generic
approach is not only applicable to text characters but to
arbitrary objects as well. In the experimental results, we
compare our approach against a pattern matching algorithm, two
classification algorithms based on contour analysis, and a
commercial OCR system. The overall recognition results are good
enough even for the indexing of low resolution images and
videos. |
| Abstract |
|
curvature scale space
CSS
convex regions
character recognition
character segmentation
|
| Keywords |
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