塑造历史：用于三千年楔形文字泥板分析与断代的先进机器学习技术
Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia
AI总结：
本文利用超过94,000张楔形文字泥板图像，通过决策树、ResNet50和变分自编码器等深度学习方法，基于泥板轮廓实现三千年历史的自动分类与断代，并开发可解释工具以探索形状演变。
摘要：
楔形文字泥板于公元前四千纪晚期左右出现在古代美索不达米亚，是人类最早的书写系统之一。这些文物以黏土泥板上的楔形刻痕为特征，为人们了解美索不达米亚文明在各个领域的情况提供了洞见。传统上，对这些泥板的分析和断代依赖于对形状和书写风格的主观评估，导致在确定其确切时间来源方面存在不确定性。近期数字化技术的进步通过提升可访问性和分析能力，彻底改变了楔形文字研究。与大多数专注于文本内容的研究不同，我们的研究独特地聚焦于泥板的轮廓，将其视为其历史时期的重要指示标志。利用来自 Cuneiform Digital Library Initiative 收藏的超过 94,000 张图像这一前所未有的数据集，我们应用深度学习方法对楔形文字泥板进行分类，覆盖超过 3,000 年的历史。通过利用统计学、计算技术以及基于变分自编码器（Variational Auto-Encoders, VAEs）的生成建模，我们在这些古代文献的自动分类方面取得了实质性进展，重点关注泥板轮廓作为关键预测因素。我们的分类方法从使用高宽比的决策树（Decision Tree）开始，最终采用 ResNet50 模型，在泥板轮廓分类上实现了 61% 的宏观 F1 分数。此外，我们引入了新颖的由 VAE 驱动的工具，以增强可解释性，并使研究人员能够探索不同时代和文类之间泥板形状的变化。这项研究通过展示大规模数据分析与统计方法相结合的价值，为文档分析和古文书学做出了贡献。这些洞见为历史学家和铭文学家提供了宝贵的工具，丰富了我们对楔形文字泥板及其创造文化的理解。
英文摘要：
Cuneiform tablets, emerging in ancient Mesopotamia around the late fourth millennium BCE, represent one of humanity's earliest writing systems. Characterized by wedge-shaped marks on clay tablets, these artifacts provided insight into Mesopotamian civilization across various domains. Traditionally, the analysis and dating of these tablets rely on subjective assessment of shape and writing style, leading to uncertainties in pinpointing their exact temporal origins. Recent advances in digitization have revolutionized the study of cuneiform by enhancing accessibility and analytical capabilities. Our research uniquely focuses on the silhouette of tablets as significant indicators of their historical periods, diverging from most studies that concentrate on textual content. Utilizing an unprecedented dataset of over 94,000 images from the Cuneiform Digital Library Initiative collection, we apply deep learning methods to classify cuneiform tablets, covering over 3,000 years of history. By leveraging statistical, computational techniques, and generative modeling through Variational Auto-Encoders (VAEs), we achieve substantial advancements in the automatic classification of these ancient documents, focusing on the tablets' silhouettes as key predictors. Our classification approach begins with a Decision Tree using height-to-width ratios and culminates with a ResNet50 model, achieving a 61% macro F1-score for tablet silhouettes. Moreover, we introduce novel VAE-powered tools to enhance explainability and enable researchers to explore changes in tablet shapes across different eras and genres. This research contributes to document analysis and diplomatics by demonstrating the value of large-scale data analysis combined with statistical methods. These insights offer valuable tools for historians and epigraphists, enriching our understanding of cuneiform tablets and the cultures that produced them.
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