Leveraging AI for Enhanced Archaeological Data Extraction: Workflows for Textual and Image-Based Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985912%3A_____%2F25%3A00635789" target="_blank" >RIV/67985912:_____/25:00635789 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68081758:_____/25:00635789

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Leveraging AI for Enhanced Archaeological Data Extraction: Workflows for Textual and Image-Based Data

Popis výsledku v původním jazyce

The digitization of archaeological archives, particularly grey literature and archival photographs, holds immense potential for knowledge discovery. However, manual processing of such data is labour-intensive and often lacks consistency, making it a prime candidate for automation. This paper presents pilot implementation of re-usable digital research workflows that integrate text and image recognition technologies and AI models to streamline the analysis of archaeological documentation. These workflows are being developed for the purposes of enhancing (meta)data quality in the Archaeological Map of the Czech Republic (AMCR) digital repository and the ARIADNE Knowledge Base and discovery service. The talk summarises the journey leading towards the implementation of both of the workflows, discusses what so far worked and what did not, including the dead ends we encountered and what we learned along the way. The current state of workflows’ implementation will be demonstrated on pilot results based on the archival textual and image documents, showcasing how AI technologies can enhance archaeological archives processing and foster further research.

Název v anglickém jazyce

Leveraging AI for Enhanced Archaeological Data Extraction: Workflows for Textual and Image-Based Data

Popis výsledku anglicky

The digitization of archaeological archives, particularly grey literature and archival photographs, holds immense potential for knowledge discovery. However, manual processing of such data is labour-intensive and often lacks consistency, making it a prime candidate for automation. This paper presents pilot implementation of re-usable digital research workflows that integrate text and image recognition technologies and AI models to streamline the analysis of archaeological documentation. These workflows are being developed for the purposes of enhancing (meta)data quality in the Archaeological Map of the Czech Republic (AMCR) digital repository and the ARIADNE Knowledge Base and discovery service. The talk summarises the journey leading towards the implementation of both of the workflows, discusses what so far worked and what did not, including the dead ends we encountered and what we learned along the way. The current state of workflows’ implementation will be demonstrated on pilot results based on the archival textual and image documents, showcasing how AI technologies can enhance archaeological archives processing and foster further research.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

60102 - Archaeology

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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