Identifying cross-depicted historical motifsShow others and affiliations
2018 (English)In: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR 2018, IEEE, 2018, p. 333-338, article id 8583783Conference paper, Published paper (Refereed)
Abstract [en]
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of identifying the same object even when it is depicted in a variety of manners.This is a common problem in handwritten historical document image analysis, for instance when the same letter or motif is depicted in several different ways.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
Place, publisher, year, edition, pages
IEEE, 2018. p. 333-338, article id 8583783
Series
International Conference on Handwriting Recognition, ISSN 2167-6445
Keywords [en]
convolutional neural network, cross-depiction, deep learning, machine learning, watermarks
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-72981DOI: 10.1109/ICFHR-2018.2018.00065ISI: 000454983200056Scopus ID: 2-s2.0-85060032898ISBN: 978-1-5386-5875-8 (print)OAI: oai:DiVA.org:ltu-72981DiVA, id: diva2:1290886
Conference
16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018, 5- August 2018, Niagara Fall, United States
2019-02-212019-02-212019-03-11Bibliographically approved