The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line recognition of handprinted characters: survey and beta tests
Pattern Recognition
The NPL electronic paper project
International Journal of Man-Machine Studies
The String-to-String Correction Problem
Journal of the ACM (JACM)
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Hand-Drawn Symbol Recognition Based on Primitives Separation and Fuzzy Inference
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
A New Fuzzy Character Segmentation Algorithm for Persian / Arabic Typed Texts
Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications: Fuzzy Days
Model-Based On-Line Handwritten Digit Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Minimum Classification Error Training for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy technique based recognition of handwritten characters
Image and Vision Computing
A new separation measure for improving the effectiveness of validity indices
Information Sciences: an International Journal
Fuzzy technique based recognition of handwritten characters
WILF'03 Proceedings of the 5th international conference on Fuzzy Logic and Applications
Identification of t–s fuzzy classifier via linear matrix inequalities
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Design of t–s fuzzy classifier via linear matrix inequality approach
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
KHATT: An open Arabic offline handwritten text database
Pattern Recognition
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This paper presents an original method for creating allograph models and recognizing them within cursive handwriting. This method concentrates on the morphological aspect of cursive script recognition. It uses fuzzy-shape grammars to define the morphological characteristics of conventional allographs which can be viewed as basic knowledge for developing a writer independent recognition system. The system uses no linguistic knowledge to output character sequences that possibly correspond to an unknown cursive word input.The recognition method is tested using multi-writer cursive random letter sequences. For a test dataset containing a handwritten cursive text 600 characters in length written by ten different writers, average character recognition rates of 84.4% to 91.6% are obtained, depending on whether only the best character sequence output of the system is considered or if the best of the top 10 is accepted. These results are achieved without any writer-dependent tuning. The same dataset is used to evaluate the performance of human readers. An average recognition rate of 96.0% was reached, using ten different readers, presented with randomized samples of each writer. The worst reader-writer performance was 78.3%. Moreover, results show that system performances are highly correlated with human performances.