Abstract

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.

Date

2012

Authors

Shiv Vitaladevuni, Fred Choi, Rohit Prasad, Premkumar Natarajan

Conference

Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)

Pages

347-350

Publisher

IEEE