Open Access

ARTICLE

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

Adéla Hamplová^1,*, Alexey Lyavdansky^2,*, 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: ; Alexey Lyavdansky. 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

Received 18 February 2024; Accepted 12 April 2024; Issue published 08 July 2024

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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Keywords

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