Nowadays, more and more printed books are accompanied by electronic resources including videos, audios, games,augmented reality and other mobile apps. However, it is not very convenient to access most of these electronic resources,as the association between printed books and electronic resources is not automatically available [2]. To build a bridgebetween a book page and the corresponding electronic resources, a large-scale book page retrieval method using deephashing network is presented in this paper. There are mainly three contributions: First, a pipeline is proposed to make aConvolutional Neural Network (CNN) trained for another unrelated task available for book page retrieval. Second, thehigh-dimensional features extracted from the CNN is mapped to the low-dimensional binary hash code sequence inHemming space by the deep hashing network, which not only increases the speed of retrieval but also saves the space offeature storage. Third, a large-scale dataset which is consist of 1.55M book page images is collected. Experimentalresults on the 1.55M book page dataset show that the proposed deep hashing network achieves a Top-1 hit rate of 92.1%and the response time is less than 0.6 second on a desktop computer with a GeForce 1080Ti GPU.
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