Oracle Character Recognition using Unsupervised Discriminative
  Consistency Network
Mei Wang Weihong Deng Sen Su
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%.