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Proceedings Paper

Efficient shape-LUT classification for document image restoration
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Paper Abstract

In previous work we showed that Look Up Table (LUT) classifiers can be trained to learn patterns of degradation and correction in historical document images. The effectiveness of the classifiers is directly proportional to the size of the pixel neighborhood it considers. However, the computational cost increases almost exponentially with the neighborhood size. In this paper, we propose a novel algorithm that encodes the neighborhood information efficiently using a shape descriptor. Using shape descriptor features, we are able to characterize the pixel neighborhood of document images with much fewer bits and so obtain an efficient system with significantly reduced computational cost. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.

Paper Details

Date Published: 19 January 2009
PDF: 10 pages
Proc. SPIE 7247, Document Recognition and Retrieval XVI, 72470N (19 January 2009); doi: 10.1117/12.806168
Show Author Affiliations
Tayo Obafemi-Ajayi, Illinois Institute of Technology (United States)
Gady Agam, Illinois Institute of Technology (United States)
Ophir Frieder, Illinois Institute of Technology (United States)

Published in SPIE Proceedings Vol. 7247:
Document Recognition and Retrieval XVI
Kathrin Berkner; Laurence Likforman-Sulem, Editor(s)

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