[Paper] Detection, description, analysis – Towards an integrated approach to heraldic representations on seals using machine learning and semantic web technologies (Linked Pasts 6 conference, 2 December 2020)

Example for the results of automatic image detection on the seal of Philippe III de Bourgogne; see http://www.sigilla.org/fr/sgdb/sceau-type/16615

From 2 December to 16 December 2020, the sixth edition of the conference Linked Pasts, organised by the Institute of Classical Studies in London, will be held online. Linked Pasts is an annual conference on the use of Linked Open Data for the study of history. This year, one of the panels of the conference, organised by the Center for Digital Humanities at the University of Saint Louis, will be dedicated to the subject of seals and Linked Open Data. The panel will include presentations on digital seal repositories, the linking of sigillographic data and its potential for the discipline of sigillography.

In this context, on 2 December, Philipp Schneider will give a paper entitled “Detection, Description, Analysis: Towards an Integrated Approach to Heraldic Representations on Seals Using Machine Learning and Semantic Web Technologies”. This presentation will give an overview of the current developments in the use of digital methods and technologies in the “Coat of Arms in Practice” project and show how these are applied also to sigillographic sources. Using seals as an example, Philipp Schneider will show how machine learning can be used to automatically detect and recognise coats of arms in large image collections and illustrate how the project intends to integrate these results with technologies of the Semantic Web in order to make heraldic data machine-readable and to integrate occurrences of digitised coats of arms coming from a wide range of historical sources and contexts within a common infrastructure in order to be studied together.

The complete programme of the panel is available online. Registration for the conference is compulsory but free of charge.


Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Search OpenEdition Search

You will be redirected to OpenEdition Search