用深度学习自动识别楔形文字，准确翻译古阿卡德语
Advanced Deep Learning Approaches for Automated Recognition of Cuneiform Symbols
- 五种深度模型在楔形文字数据集上训练并评估
- 两个模型在汉谟拉比法典1中实现高精度符号识别与翻译
- 为古语言研究提供新工具，适合考古与计算语言学者
本文提出一种全自动方法，通过先进深度学习算法识别和解读楔形文字。五个不同深度学习模型在包含楔形文字的综合数据集上进行训练，并根据准确率和精确率等关键指标评估性能。其中两种模型表现优异，进一步在汉谟拉比法典1的楔形文字样本上测试，均成功识别相关阿卡德语含义并生成精确英文翻译。未来工作将探索集成与堆叠方法，结合混合架构提升检测准确性与可靠性。本研究还探讨了古美索不达米亚语言阿卡德语与阿拉伯语之间的语言关联，强调其历史与文化联系。研究证明深度学习可融合计算语言学与考古学，助力破译古代文字，为人类历史理解与保护提供重要洞见。
原文摘要 · Abstract (English)
This paper presents a thoroughly automated method for identifying and interpreting cuneiform characters via advanced deep-learning algorithms. Five distinct deep-learning models were trained on a comprehensive dataset of cuneiform characters and evaluated according to critical performance metrics, including accuracy and precision. Two models demonstrated outstanding performance and were subsequently assessed using cuneiform symbols from the Hammurabi law acquisition, notably Hammurabi Law 1. Each model effectively recognized the relevant Akkadian meanings of the symbols and delivered precise English translations. Future work will investigate ensemble and stacking approaches to optimize performance, utilizing hybrid architectures to improve detection accuracy and reliability. This research explores the linguistic relationships between Akkadian, an ancient Mesopotamian language, and Arabic, emphasizing their historical and cultural linkages. This study demonstrates the capability of deep learning to decipher ancient scripts by merging computational linguistics with archaeology, therefore providing significant insights for the comprehension and conservation of human history.
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