Automatic Document Logo Detection

TitleAutomatic Document Logo Detection
Publication TypeConference Papers
Year of Publication2007
AuthorsZhu G, Doermann D
Conference NameThe 9th International Conference on Document Analysis and Recognition (ICDAR 2007)
Date Published2007///
Conference LocationCuritiba, Brazil

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.