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.
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. IEEE, pp.708-712, 2009, 10th International Conference on Document Analysis and Recognition, 2009. ICDAR '09. 〈http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=5277486〉. 〈10.1109/ICDAR.2009.207〉. 〈inria-00431186〉
