Cuneiform Reading Using Computer Vision Algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F22%3A91366" target="_blank" >RIV/60460709:41110/22:91366 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/10.1145/3556384.3556421" target="_blank" >https://dl.acm.org/doi/10.1145/3556384.3556421</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3556384.3556421" target="_blank" >10.1145/3556384.3556421</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cuneiform Reading Using Computer Vision Algorithms

Popis výsledku v původním jazyce

This paper presents a new method for computer-assisted recognition of horizontal strokes in photographs of cuneiform tablets with 90,52 % accuracy. The cuneiform script is the oldest attested writing system in the world, used for over three thousand years throughout the ancient Near East, primarily by the cultures of Mesopotamia (modern Iraq). It was impressed on clay tablets and engraved on stone slabs by writing strokes. Researchers have been trying to speed up the process of reading the tablets using different methods, as manual copying of the tablets and their transliteration is time consuming. This research, therefore, aims to recognize the elementary components, i.e., the strokes, of cuneiform signs from photographs of ancient cuneiform tablets, in order to enable effective OCR using the latest computer vision algorithms. The main difference between other approaches and ours is that we work directly with the two-dimensional photographs, instead of three-dimensional models, as there are many mor

Název v anglickém jazyce

Cuneiform Reading Using Computer Vision Algorithms

Popis výsledku anglicky

This paper presents a new method for computer-assisted recognition of horizontal strokes in photographs of cuneiform tablets with 90,52 % accuracy. The cuneiform script is the oldest attested writing system in the world, used for over three thousand years throughout the ancient Near East, primarily by the cultures of Mesopotamia (modern Iraq). It was impressed on clay tablets and engraved on stone slabs by writing strokes. Researchers have been trying to speed up the process of reading the tablets using different methods, as manual copying of the tablets and their transliteration is time consuming. This research, therefore, aims to recognize the elementary components, i.e., the strokes, of cuneiform signs from photographs of ancient cuneiform tablets, in order to enable effective OCR using the latest computer vision algorithms. The main difference between other approaches and ours is that we work directly with the two-dimensional photographs, instead of three-dimensional models, as there are many mor

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

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Association for Computing Machinery

ISBN

978-1-4503-9691-2

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

242-245

Název nakladatele

SPML 2022: 2022 5th International Conference on Signal Processing and Machine Learning

Místo vydání

New York, United States

Místo konání akce

Dalian, China

Datum konání akce

4. 8. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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