Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

CMES - Computer Modeling in Engineering and Sciences

ISSN

1526-1492

e-ISSN

1526-1492

Svazek periodika

140

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

20

Strana od-do

2869-2889

Kód UT WoS článku

001231178000001

EID výsledku v databázi Scopus

2-s2.0-85198637414