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Segmentation of Document Images
December 1989 (vol. 11 no. 12)
pp. 1322-1329
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.
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Index Terms:
picture processing; pattern recognition; document image segmentation; gray level distribution; maps; drawings; statistical classification task; print; background; class-conditional densities; noncontextual Bayes classifier; Besag's classifier; relaxation; Owen and Switzer's classifier; Haslett's classifier; pattern recognition; picture processing; statistical analysis
Citation:
T. Taxt, P.J. Flynn, A.K. Jain, "Segmentation of Document Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 12, pp. 1322-1329, Dec. 1989, doi:10.1109/34.41371
