Name
Abstract | ||
|---|---|---|
We propose in this paper a new framework to develop a transparent classifier able to deal with reject notions. The generated classifier can be characterized by a strong reliability without loosing good properties in generalization. We show on a musical scores recognition system that this classifier is very well suited to develop a complete document recognition system. Indeed this classifier allows them firstly to extract known symbols in a document (text for example) and secondly to validate segmentation hypotheses. Tests had been successfully performed on musical and digit symbols databases. |
Year | Venue | Keywords |
|---|---|---|
1999 | GREC | digit symbols databases,symbol classifier able,complete document recognition system,new framework,musical scores recognition system,wrong shapes,document recognition systems,segmentation hypothesis,strong reliability,good property,hypotheses testing,genetic algorithm |
Field | DocType | Volume |
Radial basis function,Pattern recognition,Segmentation,Computer science,Symbol,Speech recognition,Artificial intelligence,Classifier (linguistics),Margin classifier,Artificial neural network,Genetic algorithm,Quadratic classifier | Conference | 1941 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-41222-0 | 6 |
PageRank | References | Authors |
0.71 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Éric Anquetil | 1 | 48 | 9.94 |
| Bertrand Coüasnon | 2 | 169 | 19.22 |
| Frédéric Dambreville | 3 | 38 | 12.16 |