From Newsgroup: sci.lang

Just rediscovered this story.

https://www.jpost.com/archaeology/article-860320
JULY 9, 2025 00:00

An artificial-intelligence system has read a
Babylonian law tablet at 98 percent character
accuracy, raising hopes that tens of thousands
of clay tablets still lying untranslated in
museums could soon be opened to scholars and
the public alike.

The breakthrough is described in a study
uploaded on 7 May 2025 to the open-access arXiv
server by University of Dubai researchers Shahad
Elshehaby, Alavikunhu Panthakkan, Hussain
Al-Ahmad and Mina Al-Saad. Their paper, Advanced
Deep Learning Approaches for Automated
Recognition of Cuneiform Symbols (ID 2505.04678),
details how the team trained modern
image-recognition software to spot the wedge-shaped
impressions that record the worldrCOs earliest
written laws.

Cuneiform experts are few, and manually copying
signs from tablets the size of a hand can take
hours. By feeding the computer 14,100 cleaned
images of 235 different signs, the team taught it
to recognise nearly every mark on a test tablet
that carries the first clause of HammurabirCOs
CoderCowritten around 1754 BCE.

On held-back images the best network, an
EfficientNet variant, misread just one sign in
ten thousand. When faced with the real tablet, it
got roughly two characters wrong in a hundred; a
second model trailed behind at 89 percent.
...


https://arxiv.org/abs/2505.04678
Advanced Deep Learning Approaches for Automated
Recognition of Cuneiform Symbols

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. This
research explores the linguistic
relationships between Akkadian, an ancient
Mesopotamian language, and Arabic,
emphasizing their historical and cultural
linkages. This study demonstrates the
capability of deep learning to decipher
ancient scripts by merging computational
linguistics with archaeology, therefore
providing significant insights for the
comprehension and conservation of human
history.



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