  Historical watermark recognition is a highly practical, yet unsolved
challenge for archivists and historians. With a large number of well-defined
classes, cluttered and noisy samples, different types of representations, both
subtle differences between classes and high intra-class variation, historical
watermarks are also challenging for pattern recognition. In this paper,
overcoming the difficulty of data collection, we present a large public dataset
with more than 6k new photographs, allowing for the first time to tackle at
scale the scenarios of practical interest for scholars: one-shot instance
recognition and cross-domain one-shot instance recognition amongst more than
16k fine-grained classes. We demonstrate that this new dataset is large enough
to train modern deep learning approaches, and show that standard methods can be
improved considerably by using mid-level deep features. More precisely, we
design both a matching score and a feature fine-tuning strategy based on
filtering local matches using spatial consistency. This consistency-based
approach provides important performance boost compared to strong baselines. Our
model achieves 55% top-1 accuracy on our very challenging 16,753-class one-shot
cross-domain recognition task, each class described by a single drawing from
the classic Briquet catalog. In addition to watermark classification, we show
our approach provides promising results on fine-grained sketch-based image
retrieval.
