Efficient Shape-LUT Classification For Document Image Restoration
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work we showed that Look Up Table (LUT) classifiers can be trained to learn patterns of degradation and correction in historical document images.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. © 2009 SPIE-IS&T.
Recommended Citation
T. Obafemi-Ajayi et al., "Efficient Shape-LUT Classification For Document Image Restoration," Proceedings of SPIE - The International Society for Optical Engineering, vol. 7247, article no. 72470N, Society of Photo-optical Instrumentation Engineers, Mar 2009.
The definitive version is available at https://doi.org/10.1117/12.806168
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Document degradation models; Document image analysis; Document image enhancement; Historical documents; Image enhancement
International Standard Serial Number (ISSN)
0277-786X
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 Society of Photo-optical Instrumentation Engineers, All rights reserved.
Publication Date
20 Mar 2009
