Despite the explosion of text on the Internet, hard copy documents that have been scanned as images still play a significant
role for some tasks. The best method to perform ranked retrieval on a large corpus of document images, however, remains
an open research question. The most common approach has been to perform text retrieval using terms generated by optical
character recognition. This paper, by contrast, examines whether a scalable segmentation-free image retrieval algorithm,
which matches sub-images containing text or graphical objects, can provide additional benefit in satisfying a user’s
information needs on a large, real world dataset. Results on 7 million scanned pages from the CDIP v1.0 test collection
show that content based image retrieval finds a substantial number of documents that text retrieval misses, and that when
used as a basis for relevance feedback can yield improvements in retrieval effectiveness.