# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 3 Handwritten Chinese Character Recognition
### Deep Learning Based Recognition Methods
#### 3.2.1 Improve the Accuracy of Recognition  
In recent years, researchers have focused on improving the performance of HCCR methods by optimizing the network model, combining structural information of Chinese characters, and expanding the dataset. In 2016, [PERSON] et al. [33] studied the correlation between the area size of Chinese character strokes and the receptive fields, hyperparameters, and the total number of feature maps in the convolution network and proposed a method for selecting the receptive field size of handwritten Chinese character recognition. The theoretical and experimental results have significant reference values for the rational and effective selection of the receptive field size of the DCNN model in HCCR application. [PERSON] et al. [34] tried multi-stage feature extraction to recognize HCC. The experiments concluded that the recognition effect of multi-stage feature extraction is better than that of single-stage feature extraction and also summarized the suitable position of multi-stage feature extraction in the network. In 2017, [PERSON] et al. [35] proposed an iterative refinement module implemented by an attention-based recurrent neural network, which focuses on improving the current prediction by updating attention with previous predictions to improve the classification accuracy of similar characters further. In the same year, [PERSON] et al. [36] explored 18 models, including commonly used convolutional neural network (CNN) structures and their fused models with SVM, to identify 200 classes of HCC in ICDAR 2013. ZFNet, a single network adapted as AlexNet, achieved the highest recognition accuracy of 98.2%. The subsequent fusion to SVM did not significantly improve the performance of most of the individual networks in the experiment but provides a good reference for fusion methods that aggregate the performance of two or more networks. Refs. [37; 38] are innovative in the data preprocessing stage. [PERSON] et al. [37] use the spatial transformer network (STN) [39] to learn directly from the data to achieve the normalization of character shapes. [PERSON] et al. [38] preprocess characters by median filtering images to achieve data smoothing and noise reduction. Refs. [40; 41] are improvements to the GoogLeNet network to identify HCC. In 2019, [PERSON] et al. [40] changed the input and output layers of the original structure of GoogLeNet, then added layers for batch normalization, fragmented convolutional layers, and finally performed a profound transformation of the network. The improved model achieved 98.2% recognition accuracy in the CASIA-HWDB1.1 dataset. Later, [PERSON] et al. [41] proposed a shallow GoogLeNet network that maintains the depth of the initial structure while reducing the number of training parameters while targeting the misidentification problem of HCC by recognizing similar character sets again. In 2020, [PERSON] et al. [42] designed a two-stage hierarchical convolutional neural network to identify HCC in response to the problems of speed and accuracy of classification of large categories such as HCCR running on, for example, a CPU or the like. The first stage of the method can identify and distinguish different subsets of data, and the second stage network is trained for the classification problem within the  
Figure 4: The process of recognizing offline HCCs using deep learning-based approaches. The blue rectangle shows some typical deep learning models. corresponding set. The recognition accuracy is not particularly outstanding due to its simple architecture. In addition to the approaches mentioned above to improve the network structure, several studies have experimented with different loss functions. In 2016, [PERSON] et al. [43] used classification and similarity ranking to maximize inter-class differences and minimize intra-class differences. They used softmax and conditional log-likelihood loss as the model's loss function, which is effective in the HCCR recognition task. Similarly, to capture more inter-class and intra-class information, [PERSON] et al.