Identifying Cross-Depicted Historical Motifs
This addresses the challenge of identifying motifs in historical documents for researchers and archivists, representing a strong specific gain over existing methods.
The paper tackled the cross-depiction problem in historical document images by applying deep learning to a dataset of watermarks, achieving 96% classification accuracy and a false positive rate of 0.11 at 95% true positive rate for similarity rankings.
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 This is a common problem in handwritten historical documents image analysis, for instance when the same letter or motif is depicted in several different ways.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.