Chinese characters carry a wealth of morphological and semantic information;
therefore, the semantic enhancement of the morphology of Chinese characters has
drawn significant attention. The previous methods were intended to directly
extract information from a whole Chinese character image, which usually cannot
capture both global and local information simultaneously. In this paper, we
develop a stroke-based autoencoder(SAE), to model the sophisticated morphology
of Chinese characters with the self-supervised method. Following its canonical
writing order, we first represent a Chinese character as a series of stroke
images with a fixed writing order, and then our SAE model is trained to
reconstruct this stroke image sequence. This pre-trained SAE model can predict
the stroke image series for unseen characters, as long as their strokes or
radicals appeared in the training set. We have designed two contrasting SAE
architectures on different forms of stroke images. One is fine-tuned on
existing stroke-based method for zero-shot recognition of handwritten Chinese
characters, and the other is applied to enrich the Chinese word embeddings from
their morphological features. The experimental results validate that after
pre-training, our SAE architecture outperforms other existing methods in
zero-shot recognition and enhances the representation of Chinese characters
with their abundant morphological and semantic information.

在本文中，我们介绍了一个基于笔画的自编码器方法，用于对汉字的形态结构进行建模和预测，在零样本识别和增强汉字表示方面，取得了比现有方法更好的实验效果。