Note: Mosaicing of camera-captured document images
Computer Vision and Image Understanding
Composition of a dewarped and enhanced document image from two view images
IEEE Transactions on Image Processing
Metric Rectification to Estimate the Aspect Ratio of Camera-Captured Document Images
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Book scanner dewarping with weak 3d measurements and a simplified surface model
DGCI'08 Proceedings of the 14th IAPR international conference on Discrete geometry for computer imagery
Mobile document scanning and copying
Proceedings of the international conference on Multimedia
Monocular Template-based Reconstruction of Inextensible Surfaces
International Journal of Computer Vision
Reconstruction of shredded document based on image feature matching
Expert Systems with Applications: An International Journal
Digitization of deformed documents using a high-speed multi-camera array
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Reconstruction of 3d surface and restoration of flat document image from monocular image sequence
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Generation of learning samples for historical handwriting recognition using image degradation
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
Fast perspective recovery of text in natural scenes
Image and Vision Computing
Transform invariant text extraction
The Visual Computer: International Journal of Computer Graphics
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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.