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Abstract

The cuneiform script reveals some previously unknown aspects of our past. However, reading ancientclay tablets demands a substantial investment of time and persistent practice over a long period of time. As the fourth millennium came to a close,earlier recording methods gave way to the development of writing–the visualrepresentation of spoken language. The first language to be transcribed in written form in Mesopotamiawas Sumerian. Predominantly,the earliest tablets originate from the Uruksitein southern Mesopotamia, possibly marking its birthplace. Digitizationcuneiform documents is imperativeto boost research focused on the ancient Middle East.A few initiatives embarked upon this endeavoraround the year 2000. Nonetheless, the digitizationprocess is time-consuming due to the extensive volumeof documents, and a dependable(semi) automatic methodologyhas yet to bedeveloped. Giventhe antiquity of cuneiform script, recognizing cuneiform signs using real-world applications via two graph-based algorithms,each with complementaryruntime characteristics,remains a manual procedure. Translating cuneiform proves to be a daunting task. Only in relatively recent times hasgrammar been established scientifically, while lexical challenges remain abundant and far fromresolved. Furthermore, the majority of the Sumerian tablets have succumbed to the ravages of time, leaving behind only a handful of ancient depictions. Some of these old images have been preserved in a uniquecollection or in museums worldwide, allowing specialists to easily apply the sign detector to their cuneiform text studies. In this paper, we will discuss the categorization and analysis of clay tablets using a trained cuneiform model, employingartificial intelligence methodologies.Additionally, we will explore the methods employed, highlightingtheir strengths and weaknesses. Finally, we will propose potential directions for future research

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