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Large-Scale Document Image Retrieval and Classification with Runlength Histograms

Albert Gordo, Florent Perronnin, Ernest Valveny
We present a new document image descriptor based on multi-scale runlength histograms. This descriptor does not rely on layout analysis and can be computed efficiently. We show how this descriptor can achieve state-of-theart results on two very different public datasets in classification and retrieval tasks. Moreover, we show how we can compress and binarize these descriptors to make them suitable for large-scale applications. We can achieve state-ofthe- art results in classification using binary descriptors of as few as 16 to 64 bits.
Published in Pattern Recognition