Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Optical Chinese character recognition for low-quality document images
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Low Quality String Recognition for Factory Automation
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Enhancing Degraded Document Images via Bitmap Clustering and Averaging
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A New Robust Quadratic Discriminant Function
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
A Learning Pseudo Bayes Discriminant Method Based on Difference Distribution of Feature Vectors
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Video text recognition using feature compensation as category-dependent feature extraction
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Noisy digit classification with multiple specialist
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Cut digits classification with k-NN multi-specialist
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Robust chinese character recognition by selection of binary-based and grayscale-based classifier
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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Accurate recognition of blurred images is a practical but previously to mostly overlooked problem. In this paper, we quantify the level of noise in blurred images and propose a new modification of discriminant functions that adapts to the level of noise. Experimental results indicate that the proposed method actually enhances the existing statistical methods and has impressive ability to recognize blurred image patterns.