Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
On the exponential value of labeled samples
Pattern Recognition Letters
Classifier Adaptation with Non-representative Training Data
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Heeding More Than the Top Template
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Style-Consistency in Isogenous Patterns
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Towards a Ptolemaic Model for OCR
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Adaptive classifiers for multisource OCR
International Journal on Document Analysis and Recognition
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
OCR Voting Methods for Recognizing Low Contrast Printed Documents
DIAL '06 Proceedings of the Second International Conference on Document Image Analysis for Libraries
Modeling context as statistical dependence
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
IEEE Transactions on Information Theory - Part 2
Pattern field classification with style normalized transformation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Estimation, learning, and adaptation: systems that improve with use
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Hi-index | 0.14 |
We formalize the notion of style context, which accounts for the increased accuracy of the field classifiers reported in this journal recently. We argue that style context forms the basis of all order-independent field classification schemes. We distinguish between intraclass style, which underlies most adaptive classifiers, and interclass style, which is a manifestation of interpattern dependence between the features of the patterns of a field. We show how style-constrained classifiers can be optimized either for field error (useful for short fields like zip codes) or for singlet error (for long fields, like business letters). We derive bounds on the reduction of error rate with field length and show that the error rate of the optimal style-constrained field classifier converges asymptotically to the error rate of a style-aware Bayesian singlet classifier.