Ancient Egyptian Hieroglyphic Texts Structure Identification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00382709" target="_blank" >RIV/68407700:21230/25:00382709 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-78014-1_27" target="_blank" >https://doi.org/10.1007/978-3-031-78014-1_27</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-78014-1_27" target="_blank" >10.1007/978-3-031-78014-1_27</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Ancient Egyptian Hieroglyphic Texts Structure Identification

Popis výsledku v původním jazyce

In our project, we deal with translating texts recorded by the ancient Egyptian civilization into contemporary English. In this paper, we focus particularly on identification of text structures consisting of hieroglyphs. The identification is based on classification of segmented image blobs and their spatial relations using graph neural networks. We reached 99.8% accuracy on a dataset of facsimiles created for the shaft tomb of Menekhibnekau. The high accuracy is due to a combination of precise results achieved by the CRAFT method, additional features like the size of the hieroglyphs, including very robust topological properties of blob adjacency weighted by the learned nonlinear graph neural network scheme not relying on simple horizontal or vertical projections as used in standard OCR approaches. The graph of blob spatial relations is built using distance transform. We also propose an algorithm for a separation of hieroglyphs from mostly linear structures delineating the strips of hieroglyphs if they touch. Strips of hieroglyphs identified this way can be used to extract blobs of glyphs into reading sequences before their classification to Gardiner’s codes, transliteration and translation. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Název v anglickém jazyce

Ancient Egyptian Hieroglyphic Texts Structure Identification

Popis výsledku anglicky

In our project, we deal with translating texts recorded by the ancient Egyptian civilization into contemporary English. In this paper, we focus particularly on identification of text structures consisting of hieroglyphs. The identification is based on classification of segmented image blobs and their spatial relations using graph neural networks. We reached 99.8% accuracy on a dataset of facsimiles created for the shaft tomb of Menekhibnekau. The high accuracy is due to a combination of precise results achieved by the CRAFT method, additional features like the size of the hieroglyphs, including very robust topological properties of blob adjacency weighted by the learned nonlinear graph neural network scheme not relying on simple horizontal or vertical projections as used in standard OCR approaches. The graph of blob spatial relations is built using distance transform. We also propose an algorithm for a separation of hieroglyphs from mostly linear structures delineating the strips of hieroglyphs if they touch. Strips of hieroglyphs identified this way can be used to extract blobs of glyphs into reading sequences before their classification to Gardiner’s codes, transliteration and translation. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA24-11979S" target="_blank" >GA24-11979S: Rozpoznávání a překlad hieroglyfů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

26th International Conference on Speech and Computer, SPECOM 2024

ISBN

978-3-031-78014-1

ISSN

1611-3349

e-ISSN

1611-3349

Počet stran výsledku

16

Strana od-do

362-377

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

—

Místo konání akce

Belgrade

Datum konání akce

25. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001415334000027