Ancient history relies on the study of ancient characters. However,
real-world scanned oracle characters are difficult to collect and annotate,
posing a major obstacle for oracle character recognition (OrCR). Besides,
serious abrasion and inter-class similarity also make OrCR more challenging. In
this paper, we propose a novel unsupervised domain adaptation method for OrCR,
which enables to transfer knowledge from labeled handprinted oracle characters
to unlabeled scanned data. We leverage pseudo-labeling to incorporate the
semantic information into adaptation and constrain augmentation consistency to
make the predictions of scanned samples consistent under different
perturbations, leading to the model robustness to abrasion, stain and
distortion. Simultaneously, an unsupervised transition loss is proposed to
learn more discriminative features on the scanned domain by optimizing both
between-class and within-class transition probability. Extensive experiments
show that our approach achieves state-of-the-art result on Oracle-241 dataset
and substantially outperforms the recently proposed structure-texture
separation network by 15.1%.

提出了一种新的无监督领域适应方法，通过伪标签和约束增强一致性，从标记的人工手写神谕字符向未标记的扫描数据中传递知识，使模型在磨损、污渍和失真下更加鲁棒。同时，提出了一种无监督的转移损失，通过优化类间和类内转移概率，在扫描领域上学习更有辨别力的特征。大量实验表明，我们的方法在 Oracle-241 数据集上实现了最先进的结果，并且在最近提出的结构纹理分离网络上性能提升了 15.1%。

使用无监督判别一致性网络进行 Oracle 字符识别

Oracle Character Recognition using Unsupervised Discriminative  Consistency Network

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。