String techniques for detecting duplicates in document databases

Detecting duplicates in document image databases is a problem of growing importance. The task is made difficult by the various degradations suffered by printed documents, and by conflicting notions of what it means to be a "duplicate". To address these issues, this paper introduces a framework for clarifying and formalizing the duplicate detection problem. Four distinct models are presented, each with a corresponding algorithm for its solution adapted from the realm of approximate string matching. The robustness of these techniques is demonstrated through a set of experiments using data derived from real-world noise sources. Also described are several heuristics that have the potential to speed up the computation by several orders of magnitude.