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This recommendation aims to enhance the application of machine learning for cultural heritage professionals.

May 7, 2026Open Access

Author response of: Towards an Easy-to-Use Machine Learning Framework for Cultural Heritage Scientists. Round#2

CCChristos Chatzisavvas TPThomas Pappas PRPanagiotis RigasNational and Kapodistrian University of Athens

Key Points

This recommendation aims to enhance the application of machine learning for cultural heritage professionals.
Proposed a user-friendly framework designed for cultural heritage scientists to facilitate the use of machine learning.
Included feedback from experts to refine the framework and increase practicality.
Demonstrated the framework's potential to streamline the integration of machine learning in cultural heritage projects.
Shown to improve the accessibility of advanced analytical tools for scientists in cultural heritage.

Abstract

A recommendation of: Christos Chatzisavvas, Thomas Pappas, Panagiotis Rigas, Nikolaos Mitianoudis, George Pavlidis, Chairi Kiourt, Anestis Koutsoudis, Vassilis Katsouros, and George Ioannakis Towards an Easy-to-Use Machine Learning Framework for Cultural Heritage Scientists https://doi.org/10.5281/zenodo.19856328

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