Epigraphy increasingly turns to modern artificial intelligence (AI)
technologies such as machine learning (ML) for extracting insights from ancient
inscriptions. However, scarce labeled data for training ML algorithms severely
limits current techniques, especially for ancient scripts like Old Aramaic. Our
research pioneers an innovative methodology for generating synthetic training
data tailored to Old Aramaic letters. Our pipeline synthesizes photo-realistic
Aramaic letter datasets, incorporating textural features, lighting, damage, and
augmentations to mimic real-world inscription diversity. Despite minimal real
examples, we engineer a dataset of 250,000 training and 25,000 validation
images covering the 22 letter classes in the Aramaic alphabet. This
comprehensive corpus provides a robust volume of data for training a residual
neural network (ResNet) to classify highly degraded Aramaic letters. The ResNet
model demonstrates high accuracy in classifying real images from the 8th
century BCE Hadad statue inscription. Additional experiments validate
performance on varying materials and styles, proving effective generalization.
Our results validate the model's capabilities in handling diverse real-world
scenarios, proving the viability of our synthetic data approach and avoiding
the dependence on scarce training data that has constrained epigraphic
analysis. Our innovative framework elevates interpretation accuracy on damaged
inscriptions, thus enhancing knowledge extraction from these historical
resources.

我们的研究通过合成数据的方法，为古代亚述文字符号生成了大规模的数据集，以此训练人工神经网络以提高掌握这些古代碑文的能力。通过验证实验，我们验证了该模型在处理多样的真实场景中的能力，并证明了我们合成数据方法的可行性，避免了对稀缺的训练数据的依赖，从而提高了对受损碑文的解读准确性和对历史资源的知识提取。

深度亚拉姆语：实现合成数据范式，在铭文学中开启机器学习

Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine  Learning in Epigraphy

Ancient history relies on disciplines such as epigraphy, the study of ancient
inscribed texts, for evidence of the recorded past. However, these texts,
"inscriptions", are often damaged over the centuries, and illegible parts of
the text must be restored by specialists, known as epigraphists. This work
presents Pythia, the first ancient text restoration model that recovers missing
characters from a damaged text input using deep neural networks. Its
architecture is carefully designed to handle long-term context information, and
deal efficiently with missing or corrupted character and word representations.
To train it, we wrote a non-trivial pipeline to convert PHI, the largest
digital corpus of ancient Greek inscriptions, to machine actionable text, which
we call PHI-ML. On PHI-ML, Pythia's predictions achieve a 30.1% character error
rate, compared to the 57.3% of human epigraphists. Moreover, in 73.5% of cases
the ground-truth sequence was among the Top-20 hypotheses of Pythia, which
effectively demonstrates the impact of this assistive method on the field of
digital epigraphy, and sets the state-of-the-art in ancient text restoration.

这篇论文介绍了 Pythia，它是第一个使用深度神经网络从损坏的文本中恢复缺失字符的古代文本修复模型，在 PHI-ML 上实现了 30.1% 的字符错误率并在古代文字修复领域达到了最新水平。