Rahman, Ahmad Fuad Rezaur, Fairhurst, Michael (1997) Multi-prototype classification: Improved modelling of the variability of handwritten data using statistical clustering algorithms. Electronics Letters, 33 (14). pp. 1208-1210. ISSN 0013-5194. (doi:10.1049/el:19970848) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:17892)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
| Official URL: http://dx.doi.org/10.1049/el:19970848 |
Abstract
The principal obstacle in successfully recognising handwritten data is thr inherent degree of intra-class variability encountered, This calls for subclass modelling of handwritten data based on the statistically significant variations within the main classes. A novel multi-prototyping approach based on statistical clustering techniques is investigated as an appropriate solution to this problem and very encouraging results have been achieved.
| Item Type: | Article |
|---|---|
| DOI/Identification number: | 10.1049/el:19970848 |
| Subjects: | T Technology > TK Electrical engineering. Electronics. Nuclear engineering |
| Institutional Unit: | Schools > School of Engineering, Mathematics and Physics > Engineering |
| Former Institutional Unit: |
Divisions > Division of Natural Sciences > School of Engineering and Digital Arts Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
|
| Depositing User: | T.J. Sango |
| Date Deposited: | 21 May 2009 09:01 UTC |
| Last Modified: | 20 May 2025 10:33 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/17892 (The current URI for this page, for reference purposes) |
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