Signs of the Past, Patterns of the Present: On the Automatic Classification of Old Babylonian Cuneiform Signs

TL;DR

This paper explores machine learning techniques for classifying Old Babylonian cuneiform signs, addressing variability in data sources and digitization, and establishing a baseline for future research in this area.

Contribution

It demonstrates the application of ResNet50 for classifying Old Babylonian cuneiform signs and analyzes how dataset differences affect model performance.

Findings

ResNet50 achieved 87.1% top-1 accuracy on the dataset.

Model performance varies significantly with data source differences.

First automatic classification results on Old Babylonian cuneiform texts.

Abstract

The work in this paper describes the training and evaluation of machine learning (ML) techniques for the classification of cuneiform signs. There is a lot of variability in cuneiform signs, depending on where they come from, for what and by whom they were written, but also how they were digitized. This variability makes it unlikely that an ML model trained on one dataset will perform successfully on another dataset. This contribution studies how such differences impact that performance. Based on our results and insights, we aim to influence future data acquisition standards and provide a solid foundation for future cuneiform sign classification tasks. The ML model has been trained and tested on handwritten Old Babylonian (c. 2000-1600 B.C.E.) documentary texts inscribed on clay tablets originating from three Mesopotamian cities (Nippur, D\=ur-Abie\v{s}uh and Sippar). The presented and analysed model is ResNet50, which achieves a top-1 score of 87.1% and a top-5 score of 96.5% for signs with at least 20 instances. As these automatic classification results are the first on Old Babylonian texts, there are currently no comparable results.