HERITAGE SCIENCE AUSTRIA 2.0

LEGION

machine LEarninG-enabled Identification of archaeological Objects in the middle daNube river basin.

Decoding the history of Roman Carnuntum through automated classification of common ware pottery using cutting-edge Machine Learning.

EXPLORE PROJECT
Loading 3D Model 0%

Project News

Fragmented Knowledge & Data Overload

Loading 3D Model 0%

The Manual Bottleneck

Traditional classification of everyday ceramics is too slow for massive, fragmented archaeological datasets. Huge amounts of data remain unassessed.

Loading 3D Model 0%

Untapped Data Points

Tens of thousands of technical 2D find drawings remain unclassified and unintegrated due to the sheer volume of material.

Loading 3D Model 0%

Some finds are more equal than other finds

Funding for mass finds is often restricted. Automated solutions are required to bridge this gap.

AI-Enabled Archaeology

LEGION introduces a Semi-supervised Computer Vision Pipeline leveraging thousands of historical documents for deep material insight.

Hybrid Data Input (Photo of analog file folders in the OeAI / OeAW's archive and LSNÖ's archaeological central depot at Hainburg by Dominik Hagmann and LSNÖ, modified using Gemini 3.1 Pro and ChatGPT 5.5)

Hybrid Data Input

Processing over 70,000 2D drawings alongside their comprehensive archaeological metadata.

Human-in-the-Loop

Human-in-the-Loop (HITL)

eXplainable AI (XAI) ensures results are validated by experts to remain archaeologically robust.

Open Source Integration Visualization

Open Source Integration

Fully open-source solutions hosted on GitHub, ensuring long-term accessibility and transparency for the global research community.

New Socio-Economic Insights & AI Tools

Local Production & Trade Visualization

Local Production & Trade

Revealing complex patterns in local production and tracking trade trends across the entire Middle Danube River Basin.

New Typochronology Visualization

New Typochronology

Establishing a completely new, data-driven typochronology of Roman Common Ware based on large scale analysis.

High Classification Accuracy

Free & Open-Source Classification Tool

A user-friendly, open-source tool built for rapid, automated typochronological dating with >90% classification accuracy.

Open-Source Ecosystem & Research

LEGION is committed to Open Science, FAIR and CARE principles, and Reproducible AI. All developed machine learning pipelines, benchmark datasets, web platforms, and research findings are released openly for the global archaeological and computer vision communities.

Benchmark Dataset 507 Records CC BY 4.0 Carnuntum

CENTURIA Dataset & Pipeline

By Gissu Valentina Naghavi, Dominik Hagmann, Martin Kampel, and Irene Ballester

CENTURIA Logo (OeAI / OeAW & CVL / TU Wien)

The CENTURIA benchmark provides 507 retro-digitised Roman pottery documentation records from Carnuntum, complete with handwritten text ground truth, bounding boxes, and structured field labels across 7 metadata categories (provenance, project, find number, stratigraphic unit, pottery form, fabric, publication type, and rim diameter). Evaluates 5 state-of-the-art OCR models and provides LoRA fine-tuning workflows achieving >87% extraction accuracy.

507 Find Records
>87% Field Accuracy
<1.5% LoRA Error
🏆 Best Paper Award First Project Publication Preprint / arXiv ECCV 2026 Open Access

OCR-Based Field Extraction for Archaeological Pottery Metadata: The CENTURIA Dataset

Gissu Valentina Naghavi, Dominik Hagmann, Martin Kampel, Irene Ballester

CENTURIA Pipeline: From analogue documentation to structured metadata

Published on arXiv and presented at the Women in Computer Vision (WiCV) and Vision for Art and Culture (VISART VIII) workshops at the European Conference on Computer Vision (ECCV 2026) in Malmö, Sweden. This paper introduces the first domain-specific benchmark and lightweight LoRA fine-tuning strategy to bridge the substantial domain gap in document analysis models for retro-digitised archaeological pottery records.

BibTeX Citation ▼
@misc{naghavi2026centuria,
  title={OCR-Based Field Extraction for Archaeological Pottery Metadata: The CENTURIA Dataset},
  author={Naghavi, Gissu Valentina and Hagmann, Dominik and Kampel, Martin and Ballester, Irene},
  year={2026},
  eprint={2608.30616},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  doi={10.48550/arXiv.2608.30616},
  url={https://arxiv.org/abs/2608.30616}
}
GitHub Organization Heritage Science Austria 2.0

Collaborate with LEGION on GitHub

Explore all source code repositories, download datasets, fork our pipelines, or contribute to issues and discussions. We welcome feedback and scientific collaboration from both the archaeological and machine learning communities.

Experts Behind LEGION

The LEGION project is a multi-disciplinary collaboration bridging the gap between archaeological expertise and advanced computational methods.

We are always looking for collaboration and feedback. Reach out to the project lead for inquiries regarding data, methodology, or partnership opportunities.

OeAI Team

Austrian Archaeological Institute (OeAI / OeAW)

Dominik Hagmann PI
Silvia Radbauer Co-PI
Julia Tanzer Student Assistant
Rebekka Lederhofer Volunteer 2026
Antonia Schmid Volunteer 2026

CVL Team

Computer Vision Lab, TU Wien

Irene Ballester Campos Co-PI
Martin Kampel Co-PI
Sebastian Zambanini Co-PI
Gissu Valentina Naghavi Master's student
Anna Laczkó Master's student

Leading Institutions