Rectifying the Bound Document Image Captured by the Camera: A Model Based Approach
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Correcting Document Image Warping Based on Regression of Curved Text Lines
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Image Restoration of Arbitrarily Warped Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Flattening Curved Documents in Images
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Restoring Warped Document Images through 3D Shape Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document Image Dewarping using Robust Estimation of Curled Text Lines
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Segmentation Based Recovery of Arbitrarily Warped Document Images
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
A Methodology for Document Image Dewarping Techniques Performance Evaluation
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Geometric and shading correction for images of printed materials using boundary
IEEE Transactions on Image Processing
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
| Hi-index | 0.00 |
Dewarping of camera-captured document images is one the important preprocessing steps before feeding them to a document analysis system. Over the last few years, many approaches have been proposed for document image dewarping. Usually optical character recognition (OCR) based and/or feature based approaches are used for the evaluation of dewarping algorithms. OCR based evaluation is a good measure for the performance of a dewarping method on text regions, but it does not measure how well the dewarping algorithm works on the non-text regions like mathematical equations, graphics, or tables. Feature based evaluation methods, on the other hand, do not have this problem, however, they have following limitations: i) a lot of manual assistance is required for ground-truth generation, and ii) evaluation metrics are not sufficient to get meaningful information about dewarping quality. In this paper, we present an image based methodology for the performance evaluation of dewarping algorithms using SIFT features. For ground-truths, our method only requires scanned images of pages which have been captured by a camera. This paper introduces a vectorial performance evaluation score which gives comprehensive information for determining the performance of different dewarping methods. We have tested our performance evaluation methodology on the participating methods of CBDAR 2007 document image dewarping contest and illustrated the correctness of our method.