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Detecting near-duplicate document images using interest point matching

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4 Author(s)
Vitaladevuni, S. ; Raytheon BBN Technol., Cambridge, MA, USA ; Choi, F. ; Prasad, R. ; Natarajan, P.

We present an approach to detecting near-duplicate document images using SIFT interest point matching. Given a set of document images, a database is constructed from the SIFT features extracted from each image, stored as a kd-tree. The near-duplicates of a query image are estimated by directly matching its SIFT descriptors with the feature database. We demonstrate the approach on a challenging set of unconstrained Arabic hand and machine written images obtained from the field, consisting of 16,000+ documents. Our experiments indicate that the approach detects near-duplicates with low false alarm rate and outperforms bag-of-words based approach.

Published in:

Pattern Recognition (ICPR), 2012 21st International Conference on

Date of Conference:

11-15 Nov. 2012