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A symbol spotting approach based on the vector model and a visual vocabulary (2009).
| Content Provider | CiteSeerX |
|---|---|
| Author | Nguyen, Thi-Oanh Tabbone, Salvatore Boucher, Alain |
| Description | This content is published in 10th International Conference On Document Analysis And Recognition |
| Abstract | This paper addresses the difficult problem of symbol spotting for graphic documents. We propose an approach where each graphic document is indexed as a text document by using the vector model and an inverted file structure. The method relies on a visual vocabulary built from a shape descriptor adapted to the document level and invariant under classical geometric transforms (rotation, scaling and translation). Regions of interest selected with high degree of confidence using a voting strategy are considered as occurrences of a query symbol. Experimental results are promising and show the feasibility of our approach. |
| File Format | |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Conference Proceedings |
