Extensive Review of State-of-the-Art Classification Techniques for Cuneiform Symbol Imaging: Open Issues and Challenges

Abstract

The cuneiform script reveals some previously unknown aspects of our past. However, reading ancient clay 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 visual representation of spoken language. The first language to be transcribed in written form in Mesopotamia was Sumerian. Predominantly, the earliest tablets originate from the Uruk site in southern Mesopotamia, possibly marking its birthplace. Digitization cuneiform documents is imperative to boost research focused on the ancient Middle East. A few initiatives embarked upon this endeavor around the year 2000. Nonetheless, the digitization process is time-consuming due to the extensive volume of documents, and a dependable (semi) automatic methodology has yet to be developed. Given the antiquity of cuneiform script, recognizing cuneiform signs using real-world applications via two graph-based algorithms, each with complementary runtime characteristics, remains a manual procedure. Translating cuneiform proves to be a daunting task. Only in relatively recent times has grammar been established scientifically, while lexical challenges remain abundant and far from resolved. 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 unique collection 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, employing artificial intelligence methodologies. Additionally, we will explore the methods employed, highlighting their strengths and weaknesses. Finally, we will propose potential directions for future research.

Keywords

• Cuneiform, Sumerians Tablets
• Artificial Neural Network
• Deep Learning
• Classification Algorithms