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A hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition

Craven, Michael P.; Curtis, K. Mervyn; Hayes-Gill, Barrie H.; Thursfield, C.D.


K. Mervyn Curtis

Professor of Electronic Systems and Medical Devices

C.D. Thursfield


A technique is presented which combines rule-based and neural network pattern recognition methods in an integrated system in order to perform learning and recognition of hand-written characters and gestures in realtime.

The GesRec system is introduced which provides a framework for data acquisition, training, recognition, and gesture-to-speech transcription in a Windows environment.

A recognition accuracy of 92.5% was obtained for the hybrid system, compared to 89.6% for the neural network only and 82.7% for rules only. Training and recognition times are given for an able-bodied and a disabled user.


Craven, M. P., Curtis, K. M., Hayes-Gill, B. H., & Thursfield, C. A hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition.

Conference Name Proceedings of the Fourth IEEE International Conference on Electronics, Circuits and Systems
End Date Dec 18, 1997
Deposit Date Feb 20, 2013
Peer Reviewed Peer Reviewed
Keywords gesture recognition, dissimilarity, similarity, segmentation, text-to-speech, gesture-to-speech, sign language, 3D tracking, Augmentative and Alternative Communication, AAC, human computer interaction, HCI
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Additional Information © 1997 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


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