Sampling and quantization of bilevel signals
Pattern Recognition Letters
Document image analysis
Large-Scale Simulation Studies in Image Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Sampling of Printed Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of image degradation caused by scanning
Pattern Recognition Letters
A Statistical, Nonparametric Methodology for Document Degradation Model Validation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prediction of OCR accuracy using simple image features
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Scanner Parameter Estimation Using Bilevel Scans of Star Charts
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Digital Libraries and Document Image Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
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Document images are degraded through bilevel processes such as scanning, printing, and photocopying. The resulting image degradations can be categorized based either on observable degradation features or on degradation model parameters. The degradation features can be related mathematically to model parameters. In this paper we statistically compare pairs of populations of degraded character images created with different model parameters. The changes in the probability that the characters are from different populations when the model parameters vary correlate with the relationship between observable degradation features and the model parameters. The paper also shows which features have the largest impact on the image.