Decoding Substitution Ciphers by Means of Word Matching with Application to OCR
IEEE Transactions on Pattern Analysis and Machine Intelligence
Connected Components in Binary Images: The Detection Problem
Connected Components in Binary Images: The Detection Problem
Classifier Adaptation with Non-representative Training Data
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Style Context with Second-Order Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Style Consistent Classification of Isogenous Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards Ontology Generation from Tables
World Wide Web
Analytical Results on Style-Constrained Bayesian Classification of Pattern Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visible models for interactive pattern recognition
Pattern Recognition Letters
On a New Class of Bounds on Bayes Risk in Multihypothesis Pattern Recognition
IEEE Transactions on Computers
IEEE Transactions on Computers
A Means for Achieving a High Degree of Compaction on Scan-Digitized Printed Text
IEEE Transactions on Computers
Modeling context as statistical dependence
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
Self-corrective character recognition system
IEEE Transactions on Information Theory
Decision tree design using a probabilistic model (Corresp.)
IEEE Transactions on Information Theory
Baum's forward-backward algorithm revisited
Pattern Recognition Letters
Computing multidimensional Delaunay tessellations
Pattern Recognition Letters
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The accuracy of automated classification (labeling) of single patterns, especially printed, hand-printed, or handwritten characters, has leveled off. Further gains in accuracy require classifying sequences of patterns. Linguistic context, already widely used, relies on 1-D lexical and syntactic constraints. Style-constrained classification exploits the shape-similarity of sets of same-source (isogenous) characters of either the same or different classes. For understanding tables and forms, 2-D structural and relational constraints are necessary. Applications of pattern recognition that do not exceed the limits of human senses and cognition can benefit from green interaction wherein operator corrections are recycled to the classifier.