# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 3 Handwritten Chinese Character Recognition
### Deep Learning Based Recognition Methods
#### 3.2.3 Few-Shot Learning  
Current approaches are mainly tested on large public datasets. Handwriting recognition tasks in real applications are much more complex than in public datasets. We need to develop unique datasets based on specific research tasks. However, manual labeling is not only cumbersome but also expensive. Therefore, researchers try to perform relevant handwriting recognition tasks using a small number of labeled or unlabeled samples.  
A new neural network architecture was constructed by [PERSON] et al. [60], employing semi-supervised learning, which involves using a lot of labeled source data and little target domain data for model training. The algorithm mainly uses the domain adaptation in the transfer learning to transform the classification classifier. Finally, the effectiveness of the model was proved by testing on different data sets. [PERSON] et al. [61] proposed a dense connection structure radical analysis network for analyzing Chinese character radicals and two-dimensional structures, namely DenseRAN. In this network, an encoder converts the image into high-level visual features. Then, a decoder composed of recurrent neural networks is used to obtain the relevant radicals and two-dimensional structures to improve the model's accuracy in recognizing Chinese characters. Moreover, the method can also handle some unseen Chinese characters in the training dataset. In 2019, [PERSON] et al. [62] similarly exploited the decomposition knowledge of Chinese characters to implement the few-shot/zero-shot HCCR task. They proposed a novel radical aggregation network (RAN) consisting of a radical mapping encoder (RME), a radical aggregation module (RAM), and a character analysis decoder (CAD) to cope with the problems of insufficient representation of character radical features and inflexible decoding algorithms that affect recognition performance in [61]. RME can guarantee recognition performance for seen characters and recognize unseen characters effectively. In addition, with a small data sample, [PERSON] et al. [63] proposed a matching network to connect template characters and handwritten characters in 2020. The features taken from the template character image in place of the softmax regression layer's parameters, the network can generalize well to new Chinese characters that are not part of the training set. At the same time, they used a special structure to accomplish the prediction of similarity between HCC and template images [64].  
A novel zero-shot HCCR method combining the hierarchical knowledge of Chinese characters was proposed by [PERSON] et al. [65]. First, the tree layout of primitives is obtained by manipulating the relationship between characters and their primitives, such as radicals and structures. Then, this paper proposes a new zero-shot hierarchical decomposition embedding method for encoding tree layouts into semantic vectors. Finally, a framework based on Convolutional Neural Network (CNN) is constructed to obtain relevant semantic vectors for learning radicals and character structures, thereby obtaining recognition results. Since different Chinese characters share some common radicals and structures, the zero-shot based method is able to identify new categories without extracting any labeled samples from them. The method achieved competitive performance on conventional experimental setups and significantly outperforms state-of-the-art methods on zero-shot experimental setups. In fact, character and radical-based methods are less satisfactory for recognizing some rare characters. [PERSON] et al. [66] proposed a stroke-based character recognition method based on zero-shot learning. As shown in Figure 5, this method decomposes characters into stroke sequences through network learning and recognizes some rare characters based on matching strategies. The experimental results show good performance in character zero-shot and radial zero-shot scenarios.