Cross-depiction is the problem of identifying the same object even when it is
depicted in a variety of manners. This is a common problem in handwritten
historical documents image analysis, for instance when the same letter or motif
is depicted in several different ways...
It is a simple task for humans yet
conventional heuristic computer vision methods struggle to cope with it. In
this paper we address this problem using state-of-the-art deep learning
techniques on a dataset of historical watermarks containing images created with
different methods of reproduction, such as hand tracing, rubbing, and
radiography. To study the robustness of deep learning based approaches to the
cross-depiction problem, we measure their performance on two different tasks:
classification and similarity rankings. For the former we achieve a
classification accuracy of 96% using deep convolutional neural networks. For
the latter we have a false positive rate at 95% true positive rate of 0.11. These results outperform state-of-the-art methods by a significant margin.