Incorporating Language Syntax in Visual Text Recognition with a Statistical Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prototype Extraction and Adaptive OCR
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
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
Arabic Handwriting Recognition Using Baseline Dependant Features and Hidden Markov Modeling
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Interactive Document Processing and Digital Libraries
DIAL '06 Proceedings of the Second International Conference on Document Image Analysis for Libraries
OCR Voting Methods for Recognizing Low Contrast Printed Documents
DIAL '06 Proceedings of the Second International Conference on Document Image Analysis for Libraries
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Maximum-Likelihood Approach to Symbolic Indirect Correlation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Notes on contemporary table recognition
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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Two methods, Symbolic Indirect Correlation (SIC) and Style Constrained Classification (SCC), are proposed for recognizing handwritten Arabic and Chinese words and phrases. SIC reassembles variablelength segments of an unknown query that match similar segments of labeled reference words. Recognition is based on the correspondence between the order of the feature vectors and of the lexical transcript in both the query and the references. SIC implicitly incorporates language context in the form of letter n-grams. SCC is based on the notion that the style (distortion or noise) of a character is a good predictor of the distortions arising in other characters, even of a different class, from the same source. It is adaptive in the sense that, with a long-enough field, its accuracy converges to that of a style-specific classifier trained on the writer of the unknown query. Neither SIC nor SCC requires the query words to appear among the references.