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Adaptation Approaches in Unsupervised Learning: A Survey of the State-of-the-Art and Future Directions Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Direct Unsupervised Text Line Extraction from Colored Historical Manuscript Images Using DCT

Authors : Asim Baig, Somaya Al-Maadeed, Ahmed Bouridane, Mohamed Cheriet

Published in: Image Analysis and Recognition

Publisher: Springer International Publishing

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Abstract

Extracting lines of text from a manuscript is an important preprocessing step in many digital paleography applications. These extracted lines play a fundamental part in the identification of the author and/or age of the manuscript. In this paper we present an unsupervised approach to text line extraction in historical manuscripts that can be applied directly to a color manuscript image. Each of the red, green and blue channels are processed separately by applying DCT on them individually. One of the key advantages of this approach is that it can be applied directly to the manuscript image without any preprocessing, training or tuning steps. Extensive testing on complex Arabic handwritten manuscripts shows the effectiveness of the proposed approach.

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previous chapter Recognition of Handwritten Arabic Words with Dropout Applied in MDLSTM
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Metadata
Title
Direct Unsupervised Text Line Extraction from Colored Historical Manuscript Images Using DCT
Authors
Asim Baig
Somaya Al-Maadeed
Ahmed Bouridane
Mohamed Cheriet
Copyright Year
2016
Book
Image Analysis and Recognition

Print ISBN: 978-3-319-41500-0

Electronic ISBN: 978-3-319-41501-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-41501-7_84

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