A New Framework for Error Analysis in Computational Paleographic Dating of Greek Papyri
Giuseppe De Gregorio, Lavinia Ferretti, Rodrigo C. G. Pena, Isabelle, Marthot-Santaniello, Maria Konstantinidou, John Pavlopoulos

TL;DR
This paper introduces a new framework for error analysis in AI-based computational dating of Greek papyri, comparing it with expert palaeographers using a novel dataset, and finds that AI models perform comparably to humans.
Contribution
It presents a novel error analysis framework for computational palaeography and compares AI models with human experts on a new Greek papyri dataset.
Findings
AI model performance is comparable to human experts.
The new error analysis framework accounts for inherent dating imprecision.
The study advances methods for objective evaluation of palaeographic dating.
Abstract
The study of Greek papyri from ancient Egypt is fundamental for understanding Graeco-Roman Antiquity, offering insights into various aspects of ancient culture and textual production. Palaeography, traditionally used for dating these manuscripts, relies on identifying chronologically relevant features in handwriting styles yet lacks a unified methodology, resulting in subjective interpretations and inconsistencies among experts. Recent advances in digital palaeography, which leverage artificial intelligence (AI) algorithms, have introduced new avenues for dating ancient documents. This paper presents a comparative analysis between an AI-based computational dating model and human expert palaeographers, using a novel dataset named Hell-Date comprising securely fine-grained dated Greek papyri from the Hellenistic period. The methodology involves training a convolutional neural network on…
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Taxonomy
TopicsImage Processing and 3D Reconstruction · Ancient Mediterranean Archaeology and History · Archaeological Research and Protection
