用深度学习自动识别古巴比伦楔形文字，准确率达87.1%
Signs of the Past, Patterns of the Present: On the Automatic Classification of Old Babylonian Cuneiform Signs
- 基于ResNet50模型，对三座古城出土泥板文的楔形文字进行分类
- 在每种符号至少20次出现时，准确率高达87.1%（Top-1）
- 为未来古文字数字化与标准采集提供重要参考
本文研究了机器学习技术在楔形文字分类中的应用。由于楔形文字受书写地点、用途、书写者及数字化方式影响，存在显著差异，导致一个数据集上训练的模型难以直接迁移到另一数据集。本研究分析了这种差异对模型性能的影响，并基于尼普尔、杜尔-阿比埃苏和西帕尔三座美索不达米亚城市出土的公元前2000至1600年古巴比伦手写文书泥板，训练并评估了ResNet50模型。该模型在每种符号至少有20个实例的情况下，达到87.1%的Top-1准确率和96.5%的Top-5准确率。这些自动分类结果是首次应用于古巴比伦文本，目前尚无可比基准。
原文摘要 · Abstract (English)
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, Dūr-Abiešuh 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.
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