Detecting Treasures in Museums with Artificial Intelligence

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review


Museums around the world possess hundreds of thousands of priceless objects, which have stories to tell about human history. While students and scholars study them, even the general public is interested in these stories. If there is a way to automate the information delivery system about these objects it will be of immense value, e.g. it will support students to study these objects and speed up research. Adaptive blended learning options are conceivable, which can perfectly merge digital analysis and onsite viewing. Thus, the preparation and post-processing of studied objects is just as conceivable as the adequate acquisition of information for on-site studies. Examples of such solutions would be mobile apps and computer software that can be used for history and archaeology education as well. However, it is important to identify these objects correctly in order to build such solutions. Computer vision technologies in artifcial intelligence (AI) can be used for this. Therefore, this paper will show how AI-algorithms can be used for digital humanities in novel ways, such as for detecting museum treasures.
The objective is to identify objects in museums by using computer vision, and building a dataset of high-resolution images of important artworks which are displayed at the New Green Vault, a part of Dresden Castle.


Original languageEnglish
Title of host publicationGemeinschaften in Neuen Medien
Number of pages13
Publication statusPublished - 2020


Title23rd Conference on Communities in New Media
SubtitleFrom Hybrid Realities to Hybrid Communities
Abbreviated titleGeNeMe‘20
Conference number23
Duration7 - 9 October 2020
Degree of recognitionInternational event

External IDs

Scopus 85097745103
ORCID /0000-0003-4411-7035/work/142244448
ORCID /0000-0002-0327-6577/work/142256829



  • Artificial Intelligence, E-Learning