This thesis describes a new line segmentation method that is optimized
for medieval manuscripts. Using a thinned version of the binarized document
image, the segmentation algorithm extracts two types of salient features
from the handwritten patterns: nodes, whose distribution allows for
the detection of line axes; segments, which are labeled according to the
nodes they connect. This method obtains very good results on manuscripts
that are usually considered hard to segment because of the numerous overlapping
and touching lines. By contrast with many existing segmentation
algorithms, this method does not rely on user-entered parameters and is not
overly sensitive to the quality of the preprocessing treatments. Although
more work is required to make it resistant to fluctuating lines, this line separation
technique can already handle a large set of medieval documents and
provides a useful input to a character segmentation program. / Line segmentation techniques in off-line handwriting recognition -- Line segmentation with the profile method -- Feature-based line segmentation -- Tests and conclusions. / Department of Computer Science
| Identifer | oai:union.ndltd.org:BSU/oai:cardinalscholar.bsu.edu:123456789/196152 |
| Date | 21 July 2012 |
| Creators | Renet, Nicolas P. |
| Contributors | McGrew, J. Michael |
| Source Sets | Ball State University |
| Detected Language | English |
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