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.

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.

Ancient Greek historians have now an artificial intelligence (AI) tool to help decipher texts. Being a scholar in ancient Greek is difficult. The primary texts, on stone that may have been chipped and weathered through time, are frequently damaged beyond repair and hard to decipher, but a recent tool by Google's DeepMind hopes to solve that using artificial intelligence. The application is rather unusual because it uses AI, in a useful way outside of the technology world. DeepMind's Ithaca, a machine learning model, makes surprisingly accurate guesses at missing words and the location and dates of ancient Greek texts.

Ancient Greek historians have a new artificial intelligence (AI) tool to help decipher texts, a study released on Tuesday suggests. Being a scholar in ancient Greek is difficult. The primary texts, on stone that may have been chipped and weathered through time, are frequently damaged beyond repair and hard to decipher, but a new tool by Google's DeepMind hopes to solve that using artificial intelligence. The application is rather unusual because it uses AI, in a useful way outside of the technology world. DeepMind's Ithaca, a machine learning model, makes surprisingly accurate guesses at missing words and the location and dates of ancient Greek texts.

As if being a scholar in ancient Greek wasn't hard enough fundamentally, the primary texts they rely on are frequently damaged beyond repair, being as they are thousands of years old. Historians may have a powerful new tool in Ithaca, a machine learning model built by DeepMind that makes surprisingly accurate guesses at missing words and the location and date of the text. It's an unusual application of AI, but one that demonstrates how useful it can be outside the tech world. The problem of incomplete ancient texts goes across many disciplines in which experts work with degraded materials. The original document might be made of stone, clay, or papyrus, written in Akkadian, ancient Greek, or Linear A, and describe anything from a grocer's bill to a hero's journey.

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.

The main source of information regarding ancient Mesopotamian history and culture are clay cuneiform tablets. Despite being an invaluable resource, many tablets are fragmented leading to missing information. Currently these missing parts are manually completed by experts. In this work we investigate the possibility of assisting scholars and even automatically completing the breaks in ancient Akkadian texts from Achaemenid period Babylonia by modelling the language using recurrent neural networks.