Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F24%3A100972" target="_blank" >RIV/60460709:41110/24:100972 - isvavai.cz</a>

Result on the web

<a href="https://www.techscience.com/CMES/v140n3/57249" target="_blank" >https://www.techscience.com/CMES/v140n3/57249</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmes.2024.050791" target="_blank" >10.32604/cmes.2024.050791</a>

Result language

angličtina

Original language name

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

CMES - Computer Modeling in Engineering and Sciences

ISSN

1526-1492

e-ISSN

1526-1492

Volume of the periodical

140

Issue of the periodical within the volume

3

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

20

Pages from-to

2869-2889

UT code for WoS article

001231178000001

EID of the result in the Scopus database

2-s2.0-85198637414