Une approche de localisation de symboles non-segmentés dans des documents graphiques A symbol spotting approach in graphic documents

and key words This paper addresses the 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 (ROI) selected with high degree of confidence using a voting strategy are considered as occurrences of a query symbol. The symbol spotting problem consists in locating all instances of a symbol embedded in documents. The representation of these symbols is not straightforward by using a good shape (symbol) descriptor because they are not isolated from their context. Therefore, a common strategy for symbol spotting consists in decomposing documents into components and in applying a shape descriptor on each of them. A vectorization step is needed for most of the approaches and usually, only symbols which satisfy some conditions are retrieved (eg. convexity, connectivity, closure, ...). Our objective is to tackle the problem from a point of view where neither symbol hypothesis nor vectorization step is needed. First of all, we proposed a descriptor to represent graphic symbols and its extension to document level. Then, we exploit a traitement du signal 2009_volume 26_numero special 5 Le document ecrit 419 1. Traduit de “Shape Context”.