2009 10th International Conference on Document Analysis and Recognition (2009)
July 26, 2009 to July 29, 2009
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.
symbol descriptor, symbol spotting, visual words, graphic document
T. Nguyen, A. Boucher and S. Tabbone, "A Symbol Spotting Approach Based on the Vector Model and a Visual Vocabulary," 2009 10th International Conference on Document Analysis and Recognition(ICDAR), Barcelona, Spain, 2009, pp. 708-712.