Learning Shape Features For Document Enhancement
In previous work we showed that shape descriptor features can be used in Look Up Table (LUT) classifiers to learn patterns of degradation and correction in historical document images. The algorithm encodes the pixel neighborhood information effectively using a variant of shape descriptor. However, the generation of the shape descriptor features was approached in a heuristic manner. In this work, we propose a system of learning the shape features from the training data set by using neural networks: Multilayer Perceptrons (MLP) for feature extraction. Given that the MLP maybe restricted by a limited dataset, we apply a feature selection algorithm to generalize, and thus improve, the feature set obtained from the MLP. We validate the effectiveness and efficiency of the proposed approach via experimental results. © 2009 Copyright SPIE - The International Society for Optical Engineering.
T. Obafemi-Ajayi et al., "Learning Shape Features For Document Enhancement," Proceedings of SPIE - The International Society for Optical Engineering, vol. 7534, article no. 75340F, Society of Photo-optical Instrumentation Engineers, Mar 2010.
The definitive version is available at https://doi.org/10.1117/12.838746
Electrical and Computer Engineering
Keywords and Phrases
Artificial neural networks; Document image analysis; Historical documents; Image enhancement; Machine learning
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Article - Conference proceedings
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29 Mar 2010