Decoding Substitution Ciphers by Means of Word Matching with Application to OCR
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards a Ptolemaic Model for OCR
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
DIAL '04 Proceedings of the First International Workshop on Document Image Analysis for Libraries (DIAL'04)
Adaptive classifiers for multisource OCR
International Journal on Document Analysis and Recognition
Evaluation of Model-Based Interactive Flower Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Visual Pattern Recognition in the Years Ahead
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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
A Model-Based Interactive Object Segmentation Procedure
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
IEEE Transactions on Computers
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
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As the accuracy of conventional classifiers, based only on a static partitioning of feature space, appears to be approaching a limit, it may be useful to consider alternative approaches. Interactive classification is often more accurate then algorithmic classification, and requires less time than the unaided human. It is more suitable for the recognition of natural patterns in a narrow domain like trees, weeds or faces than for symbolic patterns like letters and phonemes. On the other hand, symbolic patterns lend themselves better to using context and style to recognize entire fields instead of individual patterns. Algorithmic learning and adaptation is facilitated by accurate statistics gleaned from large samples in the case of symbolic patterns, and by skilled human judgment in the case of natural patterns. Recent technological advances like pocket computers, camera phones and wireless networks will have greater influence on mobile, distributed, interactive recognition of natural patterns than on conventional high-volume applications like mail sorting , check reading or forms processing.