The system presented in this paper finds images and line-drawings in scanned pages; it is a crucial processing step in the creation of a large-scale system to detect and index images found in books and historic documents. Within the scanned pages that contain both text and images, the images are found through the use of SIFT-based local-features applied to the complete scanned-page. This is followed by a novel learning system to categorize the found SIFT features into either text or image. The discrimination is based on using multiple classifiers trained via AdaBoost. Through the use of this system, we improve image detection by finding more line-drawings, graphics, and photographs, as well as by reducing the number of spurious detections due to misclassified text, discolorations, and scanning artifacts.
['Shumeet Baluja', 'Michele Covell']
Finding Images and Line-Drawings in Document-Scanning Systems