## 1 Introduction  
Chinese characters emerged as hieroglyphs and naturally carry very rich morphological and semantic information. Therefore, the pictographic information of Chinese characters can be applied to explore the application of Chinese character structures, such as zero-shot Chinese character recognition [1, 2] and the generation of stylish Chinese fonts [3, 4]. The information can also facilitate many language-dependent NLP tasks, such as the recognition of Chinese names [5, 6] and the segmentation of Chinese words [7]. Zero-Shot Chinese character recognition (zero-shot CCR), as one application of Chinese character structures, has been involved in many real-world applications [2], such as handwritten signature identification [8, 9], and historical documents retrieval [10, 11]. The main idea of zero-shot CCR is to recognize the characters that have not appeared in the training set by learning the infrastructure of Chinese characters between unseen classes and seen classes. This infrastructure can be explored from radical-level [2, 12, 13, 14, 15, 16, 17] or stroke-level [1, 2]. Radical-based methods learn the infrastructure of Chinese characters by dividing them into radical sequences, and boost significant performance on zero-shot recognition. However, due to the large number of radical parts in Chinese characters, the distribution of different radical parts is usually uneven thus may cause serious class imbalance problem [1]. To tackle this challenge, [PERSON] et al. [1] used stroke-based encoding by dividing strokes into five basic categories, making breakthrough progress in zero-shot recognition. However, this stroke-based approach still has some limitations. Specifically: (i) It uses manual decomposition to label 2D image information into a series of numbers, which cannot take full advantage of the spatial information of Chinese characters. (ii) It depends heavily on the number of classified strokes, and the strokes in Chinese characters are often to be divided into twenty-eight [3] or thirty-eight [18] categories. Too many classifications may lead to imbalance problems, while too few classifications may lead to repetition [1]. Finding the best number of categories for manual decomposition of Chinese character is a time-consuming and laborious work. In short, how to find a suitable label to describe the structural information of Chinese characters is still an opening challenge. From another perspective, the reliance on labeled data has always been a major obstacle to the development of deep learning. A good labeled image database, such as ImageNet [19], is instrumental in advancing computer vision and deep learning research. Recently, this appetite for labeled data is successfully addressed in natural language processing and computer vision by self-supervised learning. BERT [20] and MAE [21], with pre-trained methods, have achieved great success on related tasks. Thus, we expect to explore a self-supervised method on Chinese character images to avoid manual labeling, which facilitates the extraction of the structural and spatial information of Chinese character images in a more comprehensive style. Self-supervised learning on images learns image representations by recovering masked images based on the encoding of input images. Inspired by Transformer, [PERSON] et al. [22] apply Transformer directly to the sequences of image patches for image recognition, and [PERSON] et al. [23] propose a masked image modeling task to pre-train Vision Transformers. Most recently, masked autoencoder (MAE) [21] masks a high proportion (75%) of the input images and performs well on downstream tasks such as image recognition and instance segmentation. However, these methods cannot be applied directly to Chinese character images, since there are great differences between Chinese character images and general images in terms of both image structure features and semantic information.