@conference {13506,
	title = {Automatic Document Logo Detection},
	booktitle = {The 9th International Conference on Document Analysis and Recognition (ICDAR 2007)},
	year = {2007},
	month = {2007///},
	pages = {864 - 868},
	address = {Curitiba, Brazil},
	abstract = {Automatic logo detection and recognition continues to be of great interest to the document retrieval community as it enables effective identification of the source of a document. In this paper, we propose a new approach to logo detection and extraction in document images that robustly classifiesand precisely localizes logos using a boosting strategy across multiple image scales. At a coarse scale, a trained Fisher classifier performs initial classification using features from document context and connected components. Each logo candidate region is further classified at successively finer scales by a cascade of simple classifiers, which allows false alarms to be discarded and the detected region to be refined. Our approach is segmentation free and layout independent. We define a meaningful evaluation metric to measure the quality of logo detection using labeled groundtruth. We demonstrate the effectiveness of our approach using a large collection of real-world documents.},
	author = {Zhu,Guangyu and David Doermann}
}
