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
Design of a neural network character recognizer for a touch terminal
Pattern Recognition
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
LeRec: a NN/HMM hybrid for on-line handwriting recognition
Neural Computation
A modal learning adaptive function neural network applied to handwritten digit recognition
Information Sciences: an International Journal
Meta-adaptation: neurons that change their mode
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
Modal learning neural networks
WSEAS Transactions on Computers
Handwriting Recognition Algorithm in Different Languages: Survey
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Off-line verification system of the handwrite signature or text, using a dynamic programming
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
HBF49 feature set: A first unified baseline for online symbol recognition
Pattern Recognition
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In this work a new method of feature extraction for an interactive and adaptive recognizer for on-line handwritten alphanumeric characters has been proposed. The system is suitable for use in conjunction with magnetic pen based devices for inputting data to a data processing system or a computer terminal. The features are extracted from dynamically changing locations of the writing device. The new method of feature extraction is simple, computationally light and fast enough for adaptive on-line use. Extracted features are robust with respect to all possible distortions like shape, size, and orientation. For simulation experiment, numerals 0-9 are used. A single hidden layer feed forward neural network trained by Quickprop algorithm, a variation of error back propagation is used for recognition. Very high recognition rates, even for highly distorted samples have been achieved confirming high generalization capability of the extracted feature set.