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.

基于深度学习和变分自编码器方法，针对库内容丰富的超过 94,000 张楔形文字片的数据集，通过研究其轮廓形状作为关键指标，成功实现了古代文献的自动分类和时代定位，为历史学家和铭刻学家提供了更深入的洞察和解释工具。

塑造历史：三千年楔形文字片的高级机器学习技术分析和测定

Shaping History: Advanced Machine Learning Techniques for the Analysis  and Dating of Cuneiform Tablets over Three Millennia

Cuneiform writing, an old art style, allows us to see into the past. Aside
from Egyptian hieroglyphs, the cuneiform script is one of the oldest writing
systems. Many historians place Hebrew's origins in antiquity. For example, we
used the same approach to decipher the cuneiform languages; after learning how
to decipher one old language, we would visit an archaeologist to learn how to
decipher any other ancient language. We propose a deep-learning-based sign
detector method to speed up this procedure to identify and group cuneiform
tablet images according to Hebrew letter content. The Hebrew alphabet is
notoriously difficult and costly to gather the training data needed for deep
learning, which entails enclosing Hebrew characters in boxes. We solve this
problem using pre-existing transliterations and a sign-by-sign representation
of the tablet's content in Latin characters. We recommend one of the supervised
approaches because these do not include sign localization: We Find the
transliteration signs in the tablet photographs by comparing them to their
corresponding transliterations. Then, retrain the sign detector using these
localized signs instead of utilizing annotations. Afterward, a more effective
sign detector enhances the alignment quality. Consequently, this research aims
to use the Yolov8 object identification pretraining model to identify Hebrew
characters and categorize the cuneiform tablets.

使用深度学习基于 Yolov8 物体识别预训练模型，识别希伯来文字并对楔形文字片进行分类。

使用 yolov8 计算机视觉模型的希伯来字母检测和楔形文字分类

Hebrew letters Detection and Cuneiform tablets Classification by using  the yolov8 computer vision model

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.

通过对主要来源于古代美索不达米亚历史文化的粘土楔形文字进行网络模型建模，研究自动补全阿奇美尼德时期巴比伦的古代阿卡德语文本中的断裂的可能性。