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 detec- tion and extraction in document images that robustly classi- fies and precisely localizes logos using a boosting strategy across multiple image scales. At a coarse scale, a trained Fisher classifier performs initial classification using fea- tures from document context and connected components. Each logo candidate region is further classified at succes- sively 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 lay- out independent. We define a meaningful evaluation met- ric to measure the quality of logo detection using labeled groundtruth. We demonstrate the effectiveness of our ap- proach using a large collection of real-world documents.