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Proceedings Paper

Using self-organizing recognition as a mechanism for rejecting segmentation errors
Author(s): R. Allen Wilkinson; Charles L. Wilson
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Paper Abstract

We have developed a method for self-organized neural network based segmentation error checking and character recognition. In this method the page is segmented, a pre-trained self- organizing classifier is used to classify the characters coming from the segmenter and these reclassified characters are used to adaptively learn the machine print font being segmented. This allows the self organizing character classifier to perform font and image quality checking while independently checking for segmentation errors. No context is used to correct segmentation or recognition errors since the page was randomly generated. In our experiments segmentation errors caused the sequential classification of segmented characters to be confused for 6.6% of the items segmented because of the splitting and merging of characters. By using self-organizing neural network classification 9.2% of these errors were corrected to produce a correct segmentation and classification rate of 93.4% overall. After self-organization correction, segmentation and classification was done to an accuracy of 99.3% with no human intervention and in all cases 99% of all segmentation errors were detected.

Paper Details

Date Published: 1 November 1992
PDF: 11 pages
Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); doi: 10.1117/12.131546
Show Author Affiliations
R. Allen Wilkinson, National Institute of Standards and Technology (United States)
Charles L. Wilson, National Institute of Standards and Technology (United States)

Published in SPIE Proceedings Vol. 1825:
Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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