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

2026-08-31

AI Summary

研究通过OCR技术解决考古陶器手写元数据转录问题，使用CENTURIA数据集和LoRA微调方法显著提高转录准确率。

Abstract

Pottery is a primary source for reconstructing the chronological and economic dimensions of past societies. Archaeologists often document ceramic finds through technical drawings and handwritten metadata. This metadata is critical for dating, provenance attribution, and cross-site comparison, but remains inaccessible to computational analysis, requiring manual transcription of every record. We investigate whether state-of-the-art document analysis models can address this task, and introduce CENTURIA, a dataset of 507 pottery records from the Roman site of Carnuntum, providing transcriptions, bounding boxes, and structured field-level labels across seven metadata categories. Benchmarking five OCR models reveals a substantial domain gap: zero-shot transcription error reaches 15-32% SpACER-M, far exceeding rates on printed archival documents, with domain-specific fields recovered in fewer than 3% of cases. LoRA fine-tuning on just 57 samples, reflecting a realistic archival annotation budget, closes this gap, reducing transcription error to below 1.5% and recovering overall field-level accuracy above 87%. Our results show that a small expert-validated fine-tuning set suffices to convert handwritten pottery documentation into structured, searchable metadata ready for archaeological databases.

Problem

• OCR
• metadata
• handwritten
• pottery
• archaeology

Innovation

• OCR
• LoRA fine-tuning
• handwritten metadata
• archaeological pottery
• transcription accuracy