Goal-oriented rectification of camera-based document images.
Identifieur interne : 000370 ( Main/Exploration ); précédent : 000369; suivant : 000371Goal-oriented rectification of camera-based document images.
Auteurs : Nikolaos Stamatopoulos [Grèce] ; Basilis Gatos ; Ioannis Pratikakis ; Stavros J. PerantonisSource :
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society [ 1941-0042 ] ; 2011.
English descriptors
- KwdEn :
- Algorithms, Artificial Intelligence, Automatic Data Processing (methods), Documentation (methods), Image Enhancement (methods), Image Interpretation, Computer-Assisted (methods), Pattern Recognition, Automated (methods), Photography (methods), Reproducibility of Results, Sensitivity and Specificity.
- MESH :
Abstract
Document digitization with either flatbed scanners or camera-based systems results in document images which often suffer from warping and perspective distortions that deteriorate the performance of current OCR approaches. In this paper, we present a goal-oriented rectification methodology to compensate for undesirable document image distortions aiming to improve the OCR result. Our approach relies upon a coarse-to-fine strategy. First, a coarse rectification is accomplished with the aid of a computationally low cost transformation which addresses the projection of a curved surface to a 2-D rectangular area. The projection of the curved surface on the plane is guided only by the textual content's appearance in the document image while incorporating a transformation which does not depend on specific model primitives or camera setup parameters. Second, pose normalization is applied on the word level aiming to restore all the local distortions of the document image. Experimental results on various document images with a variety of distortions demonstrate the robustness and effectiveness of the proposed rectification methodology using a consistent evaluation methodology that encounters OCR accuracy and a newly introduced measure using a semi-automatic procedure.
DOI: 10.1109/TIP.2010.2080280
PubMed: 20876019
Affiliations:
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Le document en format XML
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Perantonis</name></author></analytic><series><title level="j">IEEE transactions on image processing : a publication of the IEEE Signal Processing Society</title><idno type="eISSN">1941-0042</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Automatic Data Processing (methods)</term><term>Documentation (methods)</term><term>Image Enhancement (methods)</term><term>Image Interpretation, Computer-Assisted (methods)</term><term>Pattern Recognition, Automated (methods)</term><term>Photography (methods)</term><term>Reproducibility of Results</term><term>Sensitivity and Specificity</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Automatic Data Processing</term><term>Documentation</term><term>Image Enhancement</term><term>Image Interpretation, Computer-Assisted</term><term>Pattern Recognition, Automated</term><term>Photography</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Reproducibility of Results</term><term>Sensitivity and Specificity</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Document digitization with either flatbed scanners or camera-based systems results in document images which often suffer from warping and perspective distortions that deteriorate the performance of current OCR approaches. In this paper, we present a goal-oriented rectification methodology to compensate for undesirable document image distortions aiming to improve the OCR result. Our approach relies upon a coarse-to-fine strategy. First, a coarse rectification is accomplished with the aid of a computationally low cost transformation which addresses the projection of a curved surface to a 2-D rectangular area. The projection of the curved surface on the plane is guided only by the textual content's appearance in the document image while incorporating a transformation which does not depend on specific model primitives or camera setup parameters. Second, pose normalization is applied on the word level aiming to restore all the local distortions of the document image. 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Perantonis</name><name sortKey="Pratikakis, Ioannis" sort="Pratikakis, Ioannis" uniqKey="Pratikakis I" first="Ioannis" last="Pratikakis">Ioannis Pratikakis</name></noCountry><country name="Grèce"><noRegion><name sortKey="Stamatopoulos, Nikolaos" sort="Stamatopoulos, Nikolaos" uniqKey="Stamatopoulos N" first="Nikolaos" last="Stamatopoulos">Nikolaos Stamatopoulos</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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