Learning Shape Features For Document Enhancement

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descriptor features can be used in Look Up Table (LUT) classifiers to learn patterns of degradation and correction in historical document images.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. © 2009 Copyright SPIE - The International Society for Optical Engineering.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Artificial neural networks; Document image analysis; Historical documents; Image enhancement; Machine learning

International Standard Book Number (ISBN)

978-081947927-3

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

29 Mar 2010

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