Segmentation of document images
Taxt, T.
Flynn, P.J.
Jain, A.K.
Dept. of Comput. Sci., Michigan State Univ., East Lansing, MI;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Dec 1989
Volume: 11,
Issue: 12
On page(s): 1322-1329
ISSN: 0162-8828
References Cited: 21
CODEN: ITPIDJ
INSPEC Accession Number: 3572681
Digital Object Identifier: 10.1109/34.41371
Current Version Published: 2002-08-06
Abstract
Several methods for segmentation of document images (maps,
drawings, etc.) are explored. The segmentation operation is posed as a
statistical classification task with two pattern classes: print and
background. A number of classification strategies are available. All
require some prior information about the distribution of gray levels for
the two classes. Training (either supervised or unsupervised) is
employed to form these initial density estimates. Automatic updating of
the class-conditional densities is performed within subregions in the
image to adapt these global density estimates to the local image area.
After local class-conditional densities have been obtained, each pixel
is classified within the window using several techniques: a
noncontextual Bayes classifier, Besag's classifier, relaxation, Owen and
Switzer's classifier, and Haslett's classifier. Four test images were
processed. In two of these, the relaxation method performed best, and in
the other two, the noncontextual method performed best. Automatic
updating improved the results for both classifiers
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
