The work in this paper describes the training and evaluation of machine learning (ML) techniques for the classification of cuneiform signs. There is a lot of variability in cuneiform signs, depending on where they come from, for what and by whom they were written, but also how they were digitized. This variability makes it unlikely that an ML model trained on one dataset will perform successfully on another dataset. This contribution studies how such differences impact that performance. Based on our results and insights, we aim to influence future data acquisition standards and provide a solid foundation for future cuneiform sign classification tasks. The ML model has been trained and tested on handwritten Old Babylonian (c. 2000-1600 B.C.E.) documentary texts inscribed on clay tablets originating from three Mesopotamian cities (Nippur, Dūr-Abiešuh and Sippar). The presented and analysed model is ResNet50, which achieves a top-1 score of 87.1% and a top-5 score of 96.5% for signs with at least 20 instances. As these automatic classification results are the first on Old Babylonian texts, there are currently no comparable results.

Cuneiform tablets, emerging in ancient Mesopotamia around the late fourth millennium BCE, represent one of humanity's earliest writing systems. Characterized by wedge-shaped marks on clay tablets, these artifacts provided insight into Mesopotamian civilization across various domains. Traditionally, the analysis and dating of these tablets rely on subjective assessment of shape and writing style, leading to uncertainties in pinpointing their exact temporal origins. Recent advances in digitization have revolutionized the study of cuneiform by enhancing accessibility and analytical capabilities. Our research uniquely focuses on the silhouette of tablets as significant indicators of their historical periods, diverging from most studies that concentrate on textual content. Utilizing an unprecedented dataset of over 94,000 images from the Cuneiform Digital Library Initiative collection, we apply deep learning methods to classify cuneiform tablets, covering over 3,000 years of history. By leveraging statistical, computational techniques, and generative modeling through Variational Auto-Encoders (VAEs), we achieve substantial advancements in the automatic classification of these ancient documents, focusing on the tablets' silhouettes as key predictors. Our classification approach begins with a Decision Tree using height-to-width ratios and culminates with a ResNet50 model, achieving a 61% macro F1-score for tablet silhouettes. Moreover, we introduce novel VAE-powered tools to enhance explainability and enable researchers to explore changes in tablet shapes across different eras and genres. This research contributes to document analysis and diplomatics by demonstrating the value of large-scale data analysis combined with statistical methods. These insights offer valuable tools for historians and epigraphists, enriching our understanding of cuneiform tablets and the cultures that produced them.

Epigraphs Stone inscriptions are important artifacts in the field of archaeology. Although several cultures follow different methods as primary forms of writing, for example, palm leaf manuscripts by Dravidians, papyrus manuscripts by Egyptians, and Animal Hide manuscripts by several European civilizations, stone inscriptions remained a robust secondary form of writing across all the civilizations that practiced writing. This is mainly due to the robustness associated with the medium, as it is impossible to manipulate, change the stone inscriptions and very difficult to mutilate them. Therefore, several historically important documents such as treaties, grants, and tombstones are engraved in stones.

The mysterious dialect of our ancient ancestors could finally be deciphered in full thanks to artificial intelligence. A million cuneiform tablets still exist in the world, experts estimate, but these writings left behind by ancient Mesopotamians require tedious work by archaeologists to translate and catalog their contents. It has been estimated that 90 percent of cuneiform texts remain untranslated. But now, a team of German researchers has figured out a new way to train computers to recognize cuneiform and even make the contents of millennia-old tablets searchable like a website, making it possible to digitize and assemble larger libraries of these ancient texts. This could unlock previously unknown details about ancient life, as the tablets contained details about feats as significant as temple construction, all the way down to squabbles as petty as customer service complaints.

A deep learning artificial intelligence (AI) model can predict missing words, fragments and sentences from cuneiform tablets that are up to 4500 years old. Clay tablets inscribed with text written in the Akkadian language are key tools for understanding the cultures that existed in and around Mesopotamia – centred on present day Iraq – between 2500 BC and 100 AD. But the tablets' age means many are damaged, with key sections of text missing.

An artificial-intelligence program is able to predict missing words from cuneiform tablets that are up to 4,500 years old with stunning accuracy. The tablets include information about Mesopotamia from between 2500 BC and 100 AD, but missing text has hindered scientists' abilities to uncover the secrets of the ancient civilization. The AI, which was taught how to read 104 languages, was fed transcriptions of 10,000 cuneiform tablets. It accurately predicted the missing words, phrases and sentences, similarly to how the autosuggest feature on your phone suggests the next line. Mesopotamia is one of the world's oldest known civilizations and gave rise to the Sumerian, Assyrian and Babylonian empires.

A deep learning artificial intelligence (AI) model can predict missing words, fragments and sentences from cuneiform tablets that are up to 4500 years old. Clay tablets inscribed with text written in the Akkadian language are key tools for understanding the cultures that existed in and around Mesopotamia – centred on present day Iraq – between 2500 BC and 100 AD. But the tablets' age means many are damaged, with key sections of text missing.

Scientists are constantly figuring out how to expand the field of use of this incredible invention, which enables computer software to progressively improve its actions by adopting knowledge gained from previous experience. Machine learning, also referred to as artificial intelligence due to its ability to perform tasks using its own judgment, has been the subject of both praise and controversy. However, the sophisticated algorithms that have served in providing you ads on social networks might have a grand future in philology, archaeology, and linguistics. According to Émilie Pagé-Perron, a Ph.D. candidate in Assyriology at the University of Toronto, we might be closer than we thought to deciphering numerous Middle-Eastern cuneiform tablets written in Sumerian and Akkadian languages, all of which are several thousand years old. Pagé-Perron is in charge of the project officially titled Machine Translation and Automated Analysis of Cuneiform Languages, which currently operates in Frankfurt, Toronto, and Los Angeles, using combined efforts to create a program capable of translating the clay tablets.

Although cuneiform passed to other Mesopotamian cultures, which refined and altered it to suit their own languages and dialects, knowledge of how to read and write the various cuneiform scripts was gradually lost to time. In the 19th century, translators managed to decipher the writing system; and in 1872 the Assyriologist George Smith translated the most famous example of cuneiform, the Epic of Gilgamesh, a 4000-year-old poem widely believed to be the earliest surviving great work of literature. Unfortunately, translation of cuneiform tablets is still a time-consuming process and there are very few modern scholars who are able to decipher them. Sumerian is what is known as a "language isolate", one that has no genealogical relationship to any other language spoken today. But modern technology has given researchers new hope of unravelling the script imprinted on the roughly 300,000 cuneiform tablets discovered to date, of which only around 10% have been translated so far.