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Proceedings Paper

Learning shape features for document enhancement
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Paper Abstract

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descriptor features can be used in Look Up Table (LUT) classifiers to learn patterns of degradation and correction in historical document images.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.

Paper Details

Date Published: 18 January 2010
PDF: 9 pages
Proc. SPIE 7534, Document Recognition and Retrieval XVII, 75340F (18 January 2010); doi: 10.1117/12.838746
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. 7534:
Document Recognition and Retrieval XVII
Laurence Likforman-Sulem; Gady Agam, Editor(s)

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