The use of machine learning to classify symbols on cultural monuments to identify their origin and historical period.

Igor Pajura; Sylwester Korga

doi:10.35784/jcsi.8020

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Published: Dec 30, 2025

DOI: https://doi.org/10.35784/jcsi.8020

Issue Vol. 37 (2025)

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The use of machine learning to classify symbols on cultural monuments to identify their origin and historical period.
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DOI

https://doi.org/10.35784/jcsi.8020

Authors

Igor Pajura

s95514@pollub.edu.pl

Department of Computer Science, Lublin University of Technology, Poland

https://orcid.org/0009-0006-2700-0109

Sylwester Korga

s.korga@pollub.pl

Department of Computer Science, Lublin University of Technology, Poland

Abstract

Identifying and cataloguing cultural heritage objects is a time consuming process that requires expert knowledge. This study explores the application of deep learning models, specifically YOLOv8 and ResNet50, to classify historic buildings by historical epoch and country of origin, respectively. This research was conducted using a dataset of 3,200 images, which featured monuments categorized into four separate historical periods and representing four distinct nations. YOLOv8 detected buildings and classified them into historical epochs while ResNet50 was used for classifying the country of origin. The analysis demonstrated that models achieved a notable degree of effectiveness in identifying both the architectural epochs and the countries of origin.

Keywords:

Deep Learning, YOLOv8, ResNet50

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Pajura, I., & Korga, S. (2025). The use of machine learning to classify symbols on cultural monuments to identify their origin and historical period. Journal of Computer Sciences Institute, 37, 470–475. https://doi.org/10.35784/jcsi.8020

The use of machine learning to classify symbols on cultural monuments to identify their origin and historical period.

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