## 2 Related Work  
**Masked Image Encoding:** To learn image representations by predicting masked images from an input sequence of images, DAE [24] first considers masks as noises and obtain image representation by denosing the corrupted input signals. This pioneering work is generalized to a number of masked image encoding methods. Inspired by the Transformer, many approaches [22; 23; 25] are proposed for image encoding. The Vision Transformer (ViT) [22] performs a preliminary exploration of masked patch prediction for self-supervised learning. [PERSON] [23] predicts the discrete tokens [26] to pre-train ViT. Most recently, MAE [21] masks about 75% proportion of the input image and achieves better recognition accuracy on ImageNet-1K and other datasets. However, Chinese character images are quite different from general images in terms of both image structure features and semantic information. Thus, we explore a novel self-supervision method that focuses on the morphological structures of Chinese characters by reconstructing their stroke image sequences. **Zero-Shot Chinese Character Recognition:** Zero-Shot Chinese character recognition depends on the set of labeled training set of seen classes and some preliminary knowledge about the semantic relation between unseen classes and seen classes [27]. This semantic relation is often described as a high-dimensional vector, which is established from a semantic embedding space (e.g., the attribute space [28]). Specifically, most existing zero-shot learning methods learn a projection function from the visual feature space to the semantic embedding space [27], by training visual data with labels consisting only of visible classes. For zero-shot Chinese character recognition, the semantic embedding space is heavily based on the exploration of the structures of Chinese characters, which can be explored from radical-level [2; 12; 13; 14; 15; 16; 17] and stroke-level [1; 2]. These radical-based methods establish the semantic relation by dividing Chinese characters into radical sequences. However, some radicals may not appear in training sets in a data hungry condition, namely the class imbalance problem [1]. Thus, a more refined decomposition of Chinese characters' structure remains to explore. Cw2 vec [29] first exploits the stroke-level information of Chinese characters with stroke n-grams, which divides Chinese characters into five basic categories of strokes such as horizontal, vertical, left-falling, right-falling, and turning, according to the Chinese national standard GB18030-20, as shown in Figure 3. For example, the Chinese character \"@f\" can be encoded as '25135451' as shown in Figure 4. [PERSON] et al. [1] then used this stroke-based encoding as the semantic embedding of the Chinese character, making breakthrough progress in zero-shot recognition. However, this method depends heavily on the number of classified strokes and usually fails to capture adequate spatial and morphological information about Chinese characters. To fix this problem, we use the self-supervised method to explore the structure of Chinese characters without these manual decomposition. **Semantic Enhancement from the morphology of Chinese Characters:** [PERSON] et al. [29] first exploit the stroke-level information of Chinese characters with stroke n-grams. [PERSON] et al. [6] encode Chinese characters with Five-Stroke for the recognition of Chinese Named Entities. However, these human-generated encoding methods may lead to repetition, since they cannot take full advantage of the spatial information of Chinese characters. Instead of using the ID-based character embedding method, [PERSON] et al. [7] and [PERSON] et al. [30] extract the glyph information of Chinese characters for the language model based on Chinese character images. However, due to the lack of supervision, these methods cannot focus on global and local information simultaneously. Thus, we attempt to use self-supervised methods to extract their semantic information by reconstructing their stroke image sequences.