Characterising AI Models for Cataloguing

2026-07-13

AI Summary

This study addresses the longstanding reliance on manual labor in cataloging digital collections—a process hindered by low efficiency and high costs. The authors systematically evaluate the performance of various artificial intelligence models in automated cataloging tasks, employing both quantitative metrics and qualitative analysis to comprehensively assess accuracy, robustness, and applicability. Their investigation identifies the model architectures best suited for cataloging scenarios and distills a set of transferable, cross-domain principles for AI-driven cataloging. These findings offer both theoretical grounding and practical guidance for cultural heritage institutions seeking to advance their digital transformation through intelligent technologies.

Abstract

The creation of digital collections involves not only the digitisation of content, but also the creation of catalogue records for it. This often-overlooked task requires slow and costly expert manual work. In this project, we have evaluated the application of AI models to this task, comparing different implementations and models. This work includes a qualitative and quantitative evaluation of the experiments carried out, as well as recommendations on the use of AI models that go beyond the specific use case.

Problem

• cataloguing
• digital collections
• AI models
• manual work
• metadata creation

Innovation

• AI models
• cataloguing
• digital collections
• qualitative evaluation
• quantitative evaluation