Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Font Recognition Using Typographical Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Omnifont Open-Vocabulary OCR System for English and Arabic
IEEE Transactions on Pattern Analysis and Machine Intelligence
Font Recognition and Contextual Processing for More Accurate Text Recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A Principal Component Approach to Classification of Handwritten Words
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Visual inter-word relations and their use in OCR postprocessing
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Character recognition performance improvement using personal handwriting characteristics
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Style-Consistency in Isogenous Patterns
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Style consistency in pattern fields
Style consistency in pattern fields
Style-constrained quadratic field classifiers
Style-constrained quadratic field classifiers
Style Consistent Classification of Isogenous Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Style Consistent Classification of Isogenous Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Degradations and OCR Training
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Analytical Results on Style-Constrained Bayesian Classification of Pattern Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structured large margin machines: sensitive to data distributions
Machine Learning
Document image analysis for digital libraries
Proceedings of the 2006 international workshop on Research issues in digital libraries
Multi-character field recognition for Arabic and Chinese handwriting
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Analysis of whole-book recognition
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
A quantitative categorization of phonemic dialect features in context
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
Modeling context as statistical dependence
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
Interactive, mobile, distributed pattern recognition
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
A highly legible CAPTCHA that resists segmentation attacks
HIP'05 Proceedings of the Second international conference on Human Interactive Proofs
Towards versatile document analysis systems
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Transforming Japanese archives into accessible digital books
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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
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Patterns often occur as homogeneous groups or fields generated by the same source. In multisource recognition problems, such isogeny induces statistical dependencies between patterns (termed style context). We model these dependencies by second-order statistics and formulate the optimal classifier for normally distributed styles. We show that model parameters estimated only from pairs of classes suffice to train classifiers for any test field length. Although computationally expensive, the style-conscious classifier reduces the field error rate by up to 20 percent on quadruples of handwritten digits from standard NIST data sets.