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...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
1997
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/1902/ |
| _version_ | 1848790682384203776 |
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| author | Craven, Michael P. Curtis, K. Mervyn Hayes-Gill, Barrie H. Thursfield, C.D. |
| author_facet | Craven, Michael P. Curtis, K. Mervyn Hayes-Gill, Barrie H. Thursfield, C.D. |
| author_sort | Craven, Michael P. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:16:30Z |
| format | Conference or Workshop Item |
| id | nottingham-1902 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:16:30Z |
| publishDate | 1997 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-19022020-05-04T20:33:25Z https://eprints.nottingham.ac.uk/1902/ 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. 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. 1997 Conference or Workshop Item PeerReviewed Craven, Michael P., Curtis, K. Mervyn, Hayes-Gill, Barrie H. and Thursfield, C.D. (1997) A hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition. In: Proceedings of the Fourth IEEE International Conference on Electronics, Circuits and Systems, 15-18 December 1997, Cairo, Egypt. gesture recognition dissimilarity similarity segmentation text-to-speech gesture-to-speech sign language 3D tracking Augmentative and Alternative Communication AAC human computer interaction HCI |
| spellingShingle | gesture recognition dissimilarity similarity segmentation text-to-speech gesture-to-speech sign language 3D tracking Augmentative and Alternative Communication AAC human computer interaction HCI Craven, Michael P. Curtis, K. Mervyn Hayes-Gill, Barrie H. Thursfield, C.D. A hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition |
| title | A hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition |
| title_full | A hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition |
| title_fullStr | A hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition |
| title_full_unstemmed | A hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition |
| title_short | A hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition |
| title_sort | hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition |
| topic | gesture recognition dissimilarity similarity segmentation text-to-speech gesture-to-speech sign language 3D tracking Augmentative and Alternative Communication AAC human computer interaction HCI |
| url | https://eprints.nottingham.ac.uk/1902/ |