Geometric rectification of camera-captured document images.
Identifieur interne : 000B17 ( Main/Exploration ); précédent : 000B16; suivant : 000B18Geometric rectification of camera-captured document images.
Auteurs : Jian Liang [États-Unis] ; Daniel Dementhon ; David DoermannSource :
- IEEE transactions on pattern analysis and machine intelligence [ 0162-8828 ] ; 2008.
English descriptors
- KwdEn :
- Algorithms, Artifacts, Artificial Intelligence, Automatic Data Processing (methods), Documentation (methods), Image Enhancement (methods), Image Interpretation, Computer-Assisted (methods), Imaging, Three-Dimensional (methods), Pattern Recognition, Automated (methods), Photography (methods), Reproducibility of Results, Sensitivity and Specificity.
- MESH :
Abstract
Compared to typical scanners, handheld cameras offer convenient, flexible, portable, and non-contact image capture, which enables many new applications and breathes new life into existing ones. However, camera-captured documents may suffer from distortions caused by non-planar document shape and perspective projection, which lead to failure of current OCR technologies. We present a geometric rectification framework for restoring the frontal-flat view of a document from a single camera-captured image. Our approach estimates 3D document shape from texture flow information obtained directly from the image without requiring additional 3D/metric data or prior camera calibration. Our framework provides a unified solution for both planar and curved documents and can be applied in many, especially mobile, camera-based document analysis applications. Experiments show that our method produces results that are significantly more OCR compatible than the original images.
DOI: 10.1109/TPAMI.2007.70724
PubMed: 18276966
Affiliations:
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Le document en format XML
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However, camera-captured documents may suffer from distortions caused by non-planar document shape and perspective projection, which lead to failure of current OCR technologies. We present a geometric rectification framework for restoring the frontal-flat view of a document from a single camera-captured image. Our approach estimates 3D document shape from texture flow information obtained directly from the image without requiring additional 3D/metric data or prior camera calibration. Our framework provides a unified solution for both planar and curved documents and can be applied in many, especially mobile, camera-based document analysis applications. Experiments show that our method produces results that are significantly more OCR compatible than the original images.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Washington (État)</li></region></list><tree><noCountry><name sortKey="Dementhon, Daniel" sort="Dementhon, Daniel" uniqKey="Dementhon D" first="Daniel" last="Dementhon">Daniel Dementhon</name><name sortKey="Doermann, David" sort="Doermann, David" uniqKey="Doermann D" first="David" last="Doermann">David Doermann</name></noCountry><country name="États-Unis"><region name="Washington (État)"><name sortKey="Liang, Jian" sort="Liang, Jian" uniqKey="Liang J" first="Jian" last="Liang">Jian Liang</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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