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%。