Learning shape features for document enhancement

Tayo Obafemi-Ajayi; Gady Agam; Ophir Frieder

doi:10.1117/12.838746

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18 January 2010 Learning shape features for document enhancement

Tayo Obafemi-Ajayi, Gady Agam, Ophir Frieder

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Proceedings Volume 7534, Document Recognition and Retrieval XVII; 75340F (2010) https://doi.org/10.1117/12.838746
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

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descriptor features can be used in Look Up Table (LUT) classifiers to learn patterns of degradation and correction in historical document images.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

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Tayo Obafemi-Ajayi, Gady Agam, and Ophir Frieder "Learning shape features for document enhancement", Proc. SPIE 7534, Document Recognition and Retrieval XVII, 75340F (18 January 2010); https://doi.org/10.1117/12.838746

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