Advanced Deep Learning Approaches for Automated Recognition of Cuneiform Symbols 1 st Shahad Elshehaby College of Engineering and IT University of Dubai Dubai, United Arab Emirates s0000002884@ud.ac.ae 2 nd Alavikunhu Panthakkan College of Engineering and IT University of Dubai Dubai, United Arab Emirates apanthakkan@ud.ac.ae 3 rd Hussain Al-Ahmad College of Engineering and IT University of Dubai Dubai, United Arab Emirates halahmad@ud.ac.ae 4 th Mina Al-Saad College of Engineering and IT University of Dubai Dubai, United Arab Emirates malsaad@ud.ac.ae Abstract --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.