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. © 2009 IEEE.
CITATION STYLE
Nguyen, T. O., Tabbone, S., & Boucher, A. (2009). A symbol spotting approach based on the vector model and a visual vocabulary. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR (pp. 708–712). https://doi.org/10.1109/ICDAR.2009.207
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