Curves and Surfaces for Computer-Aided Geometric Design: A Practical Code
Curves and Surfaces for Computer-Aided Geometric Design: A Practical Code
Digital Image Warping
Document Image De-warping for Text/Graphics Recognition
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
SURFACES FOR COMPUTER-AIDED DESIGN OF SPACE FORMS
SURFACES FOR COMPUTER-AIDED DESIGN OF SPACE FORMS
A Cylindrical Surface Model to Rectify the Bound Document Image
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Estimation of 3D Shape of Warped Document Surface for Image Restoration
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Composition of a dewarped and enhanced document image from two view images
IEEE Transactions on Image Processing
Correcting bound document images based on automatic and robust curved text lines estimation
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
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
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Recently, high resolution digital cameras have made the digitization process more flexible and convenient than traditional scanning technology. Therefore, document image analysis techniques need lo be extended to handle images captured using ordinary hand-held cameras under an uncontrolled environment. In this paper, we propose an image restoration technique that corrects various warping distortions such as rolling curl, binding curl and fold distortions in camera images of geometrically distorted documents. Our goal is to restore these arbitrarily warped document images to a planar shape so as to facilitate OCR. Our method uses Spline interpolation technique based on a ruled surface model constructed from text lines in the 2 0 document image. Comparisons on OCR performance using Microsoff imaging software with two existing methods show an encouraging improvement in terms of recall and precision. The OCR precision on the corrected images using our method is improved up to 21.8% comparing to original images with a resolution of 3 megapixels.