Automatic Filter Selection Using Image Quality Assessment
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
Machine Printed Text and Handwriting Identification in Noisy Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Restoring Warped Document Images through 3D Shape Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.00 |
We estimate, using synthetically generated images, the smallest changes in document image quality that can be distinguished reliably and fully automatically by Kanungo's bootstrapping test [Kan96]. Six parameters of a physics-based document-image degradation model [Bai92] are varied, one at a time: for each, over a range of parameter-value differences, two sets of synthetic images are generated pseudorandomly and the two sets tested for statistical equivalence using Kanungo's method. The rate at which Kanungo's method rejects the hypothesis that the two sets are drawn from the same distribution is analyzed as a function of parameter difference (a specialized ``power function''). The finest discriminations afforded by the method are given by the width of the power function at a low fixed reject threshold. The data show that remarkably fine discriminations are possible -- often subtler than are evident to visual inspection -- for all six parameters. As few as 25 reference images are sufficient. These results suggest that Kanungo's method is sufficiently sensitive to a wide range of physics-based image degradations to serve as an engineering foundation for many image-quality estimation and OCR engineering purposes.