Modelizing character allographs in omni-scriptor frame: a new non-supervised clustering algorithm
✍ Scribed by Lionel Prevost; Maurice Milgram
- Book ID
- 104305005
- Publisher
- Elsevier Science
- Year
- 2000
- Tongue
- English
- Weight
- 187 KB
- Volume
- 21
- Category
- Article
- ISSN
- 0167-8655
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✦ Synopsis
The problem of the allographs'' speci®c of the dynamic handwriting in omni-scriptor context renders the implementation of classical'' clustering algorithms particularly delicate because it introduces the notion of heterogeneous classes characterized by strongly variable example densities. We propose here a hybrid clustering algorithm combining both a prototype placement stage and an adaptation stage. The process reduces drastically the number of references to be examined during a k-nn classi®cation while preserving to the classi®er a high level of performance. The experience has been driven on an extensive alphabet including 80 classes. Recognition rates, evaluated on nearly 35 000 examples from the UNIPEN basis show the reliability of the modelization.