Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Latex: a document preparation system
Latex: a document preparation system
Validation of Image Defect Models for Optical Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document degradation models and a methodology for degradation model validation
Document degradation models and a methodology for degradation model validation
TEX: The Program
Structured Document Image Analysis
Structured Document Image Analysis
Relating Statistical Image Differences and Degradation Features
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
A Bilingual OCR for Hindi-Telugu Documents and its Applications
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Bible and multilingual optical character recognition
Communications of the ACM - 3d hard copy
Robust and Accurate Vectorization of Line Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hierarchical, HMM-based automatic evaluation of OCR accuracy for a digital library of books
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
A progressive learning method for symbols recognition
Proceedings of the 2007 ACM symposium on Applied computing
Effect of OCR error correction on Arabic retrieval
Information Retrieval
DIAR: Advances in Degradation Modeling and Processing
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Measure of circularity for parts of digital boundaries and its fast computation
Pattern Recognition
Gabor filters-based feature extraction for character recognition
Pattern Recognition
Multi-resolution character recognition by adaptive classification
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Reconstruction of shredded document based on image feature matching
Expert Systems with Applications: An International Journal
Display text segmentation after learning best-fitted OCR binarization parameters
Expert Systems with Applications: An International Journal
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
RANVEC and the arc segmentation contest: second evaluation
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
A semi-automatic adaptive OCR for digital libraries
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
CMS'12 Proceedings of the 13th IFIP TC 6/TC 11 international conference on Communications and Multimedia Security
Morphological filtering on graphs
Computer Vision and Image Understanding
Report on the symbol recognition and spotting contest
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
Document noise removal using sparse representations over learned dictionary
Proceedings of the 2013 ACM symposium on Document engineering
Generation of learning samples for historical handwriting recognition using image degradation
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
An efficient parametrization of character degradation model for semi-synthetic image generation
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
Hi-index | 0.15 |
Printing, photocopying, and scanning processes degrade the image quality of a document. Statistical models of these degradation processes are crucial for document image understanding research. Models allow us to predict system performance, conduct controlled experiments to study the breakdown points of the systems, create large multilingual data sets with groundtruth for training classifiers, design optimal noise removal algorithms, choose values for the free parameters of the algorithms, and so on. Although research in document understanding started many decades ago, only two document degradation models have been proposed thus far. Furthermore, no attempts have been made to statistically validate these models. In this paper, we present a statistical methodology that can be used to validate local degradation models. This method is based on a nonparametric, two-sample permutation test. Another standard statistical device驴the power function驴is then used to choose between algorithm variables such as distance functions. Since the validation and the power function procedures are independent of the model, they can be used to validate any other degradation model. A method for comparing any two models is also described. It uses p-values associated with the estimated models to select the model that is closer to the real world.