Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Writer Adaptation for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
The IRESTE On/Off (IRONOFF) Dual Handwriting Database
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
On-Line Adaptation in Recognition of Handwritten Alphanumeric Characters
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Integration of an On-Line Handwriting Recognition System in a Smart Phone Device
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Comparing Adaptation Techniques for On-Line Handwriting Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Self-Supervised Writer Adaptation using Perceptive Concepts: Application to On-Line Text Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
The shape of fuzzy sets in adaptive function approximation
IEEE Transactions on Fuzzy Systems
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We present an automatic on-line adaptation mechanism to the writer's handwriting style for the recognition of isolated handwritten characters. The classifier is based on a Fuzzy Inference System (FIS). This FIS is composed of fuzzy prototypes which represent the intrinsic properties of the classes and it uses numeric conclusions. The proposed adaptation mechanism affects both the conclusions of the rules and the fuzzy prototypes of the premises by recentering and re-shaping them. Doing so, the FIS is automatically fitted to the handwriting style of the writer that is currently using the system. This adaptation mechanism has been tested with 8 different writers. The results show the adaptation mechanism is able to improve the recognition rate from 88% to 98.2% in average for the 26 Latin letters.