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Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

Adéla Hamplová1,*, Alexey Lyavdansky2,*, Tomáš Novák1, Ondřej Svojše1, David Franc1, Arnošt Veselý1

1 Czech University of Life Sciences in Prague (CULS), Faculty of Economics and Management, Department of Information Engineering, Prague, 16500, Czech Republic
2 National Research University Higher School of Economics (HSE), Faculty of Humanities, Institute for Oriental and Classical Studies, Moscow, 101000, The Russian Federation

* Corresponding Authors: Adéla Hamplová. Email: email; Alexey Lyavdansky. Email: email

(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)

Computer Modeling in Engineering & Sciences 2024, 140(3), 2869-2889. https://doi.org/10.32604/cmes.2024.050791

Abstract

This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions, employing two state-of-the-art deep learning algorithms, namely YOLOv8 and Roboflow 3.0. The goal is to contribute to the preservation and understanding of historical texts, showcasing the potential of modern deep learning methods in archaeological research. Our research culminates in several key findings and scientific contributions. We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context. We also created and annotated an extensive dataset of Palmyrene inscriptions, a crucial resource for further research in the field. The dataset serves for training and evaluating the segmentation models. We employ comparative evaluation metrics to quantitatively assess the segmentation results, ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks. Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research. The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.

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Cite This Article

APA Style
Hamplová, A., Lyavdansky, A., Novák, T., Svojše, O., Franc, D. et al. (2024). Instance segmentation of characters recognized in palmyrene aramaic inscriptions. Computer Modeling in Engineering & Sciences, 140(3), 2869-2889. https://doi.org/10.32604/cmes.2024.050791
Vancouver Style
Hamplová A, Lyavdansky A, Novák T, Svojše O, Franc D, Veselý A. Instance segmentation of characters recognized in palmyrene aramaic inscriptions. Comput Model Eng Sci. 2024;140(3):2869-2889 https://doi.org/10.32604/cmes.2024.050791
IEEE Style
A. Hamplová, A. Lyavdansky, T. Novák, O. Svojše, D. Franc, and A. Veselý "Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions," Comput. Model. Eng. Sci., vol. 140, no. 3, pp. 2869-2889. 2024. https://doi.org/10.32604/cmes.2024.050791



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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