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A Symbol Spotting Approach Based on the Vector Model and a Visual Vocabulary

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.
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Submitted on : Tuesday, November 10, 2009 - 6:14:51 PM
Last modification on : Friday, August 5, 2022 - 2:38:10 PM
Long-term archiving on: : Thursday, June 17, 2010 - 6:00:53 PM


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Thi Oanh Nguyen, Salvatore Tabbone, Alain Boucher. A Symbol Spotting Approach Based on the Vector Model and a Visual Vocabulary. 10th International Conference on Document Analysis and Recognition - ICDAR 2009, Jul 2009, Barcelona, Spain. pp.708-712, ⟨10.1109/ICDAR.2009.207⟩. ⟨inria-00431186⟩



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