  Oracle bone script is the earliest-known Chinese writing system of the Shang
dynasty and is precious to archeology and philology. However, real-world
scanned oracle data are rare and few experts are available for annotation which
make the automatic recognition of scanned oracle characters become a
challenging task. Therefore, we aim to explore unsupervised domain adaptation
to transfer knowledge from handprinted oracle data, which are easy to acquire,
to scanned domain. We propose a structure-texture separation network (STSN),
which is an end-to-end learning framework for joint disentanglement,
transformation, adaptation and recognition. First, STSN disentangles features
into structure (glyph) and texture (noise) components by generative models, and
then aligns handprinted and scanned data in structure feature space such that
the negative influence caused by serious noises can be avoided when adapting.
Second, transformation is achieved via swapping the learned textures across
domains and a classifier for final classification is trained to predict the
labels of the transformed scanned characters. This not only guarantees the
absolute separation, but also enhances the discriminative ability of the
learned features. Extensive experiments on Oracle-241 dataset show that STSN
outperforms other adaptation methods and successfully improves recognition
performance on scanned data even when they are contaminated by long burial and
careless excavation.
