Logo spotting for document categorization

Logo spotting is of a great interest because it enables to categorize the document images of a digital library of scanned documents according to their sources, without any costly semantic analysis of their textual transcript. In this paper, we present an approach for logo spotting, based on the matching of keypoints extracted both from the query document images and a given set of logos (gallery) using SIFT. In order to filter the matching points and keep only the most relevant, we compare the spatial distribution of the matching keypoints in the query image and in the logo gallery. We test our approach using a large collection of real world documents using a well-known benchmark database of logos and show that our approach achieves good performances compared to state-of-the-art approaches.