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

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

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 acquisiti...

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Bibliographic Details
Main Authors: Craven, Michael P., Curtis, K. Mervyn, Hayes-Gill, Barrie H., Thursfield, C.D.
Format: Conference or Workshop Item
Published: 1997
Subjects:
gesture recognition
dissimilarity
similarity
segmentation
text-to-speech
gesture-to-speech
sign language
3D tracking
Augmentative and Alternative Communication
AAC
human computer interaction
HCI
Online Access:https://eprints.nottingham.ac.uk/1902/
Description
Summary: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.

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