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.

本研究解决了楔形文字识别与解释的难题，提出了一种全面自动化的方法，利用先进的深度学习算法来识别楔形字符。通过在综合数据集上训练五种不同的深度学习模型，并进行评估，发现其中两种模型在精度和准确率上表现出色，显著提高了对楔形符号的理解和翻译的准确性，具有重要的历史和文化价值。

自动识别楔形文字的高级深度学习方法