Design of a neural network character recognizer for a touch terminal
Pattern Recognition
High Accuracy Optical Character Recognition Using Neural Networks with Centroid Dithering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelizing character allographs in omni-scriptor frame: a new non-supervised clustering algorithm
Pattern Recognition Letters
A Hybrid Classifier for Recognizing Handwritten Numerals
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Static and Dynamic Classifier Fusion for Character Recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Writer Adaptation of Online Handwriting Models
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Constraint Tangent Distance for On-Line Character Recognition
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Speeding Up the Decision Making of Support Vector Classifiers
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
An Activation-Verification Model for On-Line Texts Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
On-line handwritten digit recognition based on trajectory and velocity modeling
Pattern Recognition Letters
A cascade of boosted generative and discriminative classifiers for vehicle detection
EURASIP Journal on Advances in Signal Processing
Action recognition with global features
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Protein fold recognition with combined SVM-RDA classifier
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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Handwriting recognition for hand-held devices like PDAs requires very accurate and adaptive classifiers. It is such a complex classification problem that it is quite usual now to make co-operate several classification methods. In this paper, we present an original two stages recognizer. The first stage is a model-based classifier which store an exhaustive set of character models. The second stage is a pairwise classifier which separate the most ambiguous pairs of classes. This hybrid architecture is based on the idea that the correct class almost systematically belongs to the two more relevant classes found by the first classifier. Experiments on a 80,000 examples database show a 30% improvement on a 62 classes recognition problem. Moreover, we show experimentally that such an architecture suits perfectly for incremental classification.