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

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.

• Type: Journal
• Year: 2009
• Where: ICDAR
• Authors: Thi-Oanh Nguyen, Salvatore Tabbone, Alain Boucher
• Keywords: Document, Document Analysis, Graphic Document, ICDAR 2009, Inverted File Structure