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Identifying Cross-Depicted Historical Motifs

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Pondenkandath Vinaychandran, Alberti Michele, Eichenberger Nicole, Ingold Rolf, Liwicki Marcus,
Project HisDoc III : Large-Scale Historical Document Classification
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Original article (peer-reviewed)

Journal 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR)
Page(s) 333 - 338
Title of proceedings 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR)
DOI 10.1109/icfhr-2018.2018.00065

Abstract

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of identifying the same object even when it is depicted in a variety of manners.This is a common problem in handwritten historical document image analysis, for instance when the same letter or motif is depicted in several different ways.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 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\% recall of 0.11. These results outperform state-of-the-art methods by a significant margin.
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