Title :
Document page segmentation by integrating distributed soft decisions
Author :
Etemad, Kamran ; Chellappa, Rama ; Doermann, David
fDate :
27 Jun- 2 Jul 1994
Abstract :
A new algorithm for image segmentation is suggested. The suggested algorithm is based on making local soft decisions on small blocks and integrating them to reduce their “ambiguities” and increase their “confidence” as more contextual evidence is obtained from the image data. Integration is performed by vote propagation across windows (within and across scales) using majorities of weighted votes. The algorithm has been tested on some document page decomposition tasks; the results of these tests are presented. This method is based on parallel, distributed and independent computations and has low complexity. It is general and can be applied to different segmentation and classification tasks
Keywords :
document image processing; image segmentation; neural nets; ambiguity reduction; classification; confidence; contextual evidence; document page decomposition tasks; document page segmentation; image segmentation; integrating distributed soft decisions; local soft decisions; parallel distributed independent computations; vote propagation; Automation; Educational institutions; Graphics; Image segmentation; Layout; Optical character recognition software; Signal processing; Testing; Uncertainty; Voting;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374857