Validation of Image Defect Models for Optical Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical, Nonparametric Methodology for Document Degradation Model Validation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document Image Quality: Making Fine Discriminations
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
A Two-State Markov Chain Model of Degraded Document Images
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Estimation of morphological degradation model parameters
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
The Bible and multilingual optical character recognition
Communications of the ACM - 3d hard copy
Restoring images with a multiscale neural network based technique
Proceedings of the 2008 ACM symposium on Applied computing
DIAR: Advances in Degradation Modeling and Processing
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Behaviour-based clustering of neural networks applied to document enhancement
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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Abstract--Noise models are crucial for designing image restoration algorithms, generating synthetic training data, and predicting algorithm performance. There are two related but distinct estimation scenarios. The first is model calibration, where it is assumed that the input ideal bitmap and the output of the degradation process are both known. The second is the general estimation problem, where only the image from the output of the degradation process is given. While researchers have addressed the problem of calibration of models, issues with the general estimation problems have not been addressed in the literature. In this paper, we describe a parameter estimation algorithm for a morphological, binary, page-level image degradation model. The inputs to the estimation algorithm are 1) the degraded image and 2) information regarding the font type (italic, bold, serif, sans serif). We simulate degraded images using our model and search for the optimal parameter by looking for a parameter value for which the local neighborhood pattern distributions in the simulated image and the given degraded image are most similar. The parameter space is searched using a direct search optimization algorithm. We use the p{\hbox{-}}\rm value of the Kolmogorov-Smirnov test as the measure of similarity between the two neighborhood pattern distributions. We show results of our algorithm on degraded document images.