## 2 Related Work  
### Deep Learning Chinese Character Recognition  
Optical character recognition (OCR) is a mainstream text recognition technology. Through the analysis and comparison of a pre-built database, various texts can be quickly recognized. With the tremendous advancement of deep learning-based computer vision technology in recent years, the convolutional neural network (CNN) approaches have been greatly applied to OCR. [PERSON] et al. [6] proposed an end-to-end subtitle recognition system. After inputting a video with subtitles, the subtitle area is marked, and sliding windows are used to cut the characters one by one at regular intervals and recognize them. Although an end-to-end system is adopted, the sliding window can easily cut out too many repetitive characters and cause failure. [PERSON] et al. [7] proposed an HCCR-GoogLeNet, which reduces the parameters of the original GoogLeNet [5], and adds the traditional feature extraction method HoG, and obtain gradient feature maps to enhance the performance of CNN. The recognition rate of the model evaluated with the Chinese Academy of Sciences handwriting databases (CASIA-HWBB) dataset [8] attained 96.74%. [PERSON] et al. [9] proposed new architecture to avoid the overfitting problem under a multi-classification problem. By using global average pooling (GAP) to replace the original fully connected (FC) layer, it can reduce the number of parameters while maintaining the original effect. [PERSON] et al. [10] resolved the disadvantage of the traditional CNN, which would require huge computation resources, and improved the recognition effect by increasing the output layer of the intermediate layer. However, GAP will cause a significant decrease in accuracy, hence they added trainable weights to GAP to solve this problem which is called global weighted average pooling (GWAP). The model achieved an accuracy of 97.1% for the CASIA-HWBB1.1 [8] dataset. [PERSON] et al. [11] proposed a new technology, called adaptive drop weight (ADW), which can effectively reduce the number of CNN parameters, and proposed global supervised low-rank expansion (GSLRE) to accelerate the entire CNN model. Compared to other baselines of handwritten Chinese character recognition (HCCR) models, the model reduces the amount of calculation by nine times and compresses the parameter amount to 1/18, but the accuracy rate is only reduced by 0.21%. With its extremely low parameter amount, it can be deployed on a mobile device to achieve rapid recognition. [PERSON] et al. [12] proposed new architecture in 2019 to improve the classification accuracy of the output layer by modifying GWAP, and extracting the class activation map (CAM) to perform character recognition. This model was evaluated  
Figure 2: Flowchart of the proposed large-scale Chinese character recognition system: (**a**) Training phase and (**b**) recognition phase. with the CASIA-HWDB1.1 dataset [8], and the recognition accuracy rate reached 97.61%, breaking the record of the year. [PERSON] et al. [13] developed a device that could be worn on the hand. This model can accurately identify 6000 Chinese characters and achieves an accuracy of 96.75% in the Chinese FingerReader testing dataset. However, its research also shows that the overall recognition rate will be affected if it encounters serious background interference. [PERSON] et al. [14] suggested a new text-classification approach based on graph neural networks, which learned word and document embedding by modeling an entire corpus as a heterogeneous graph. The results showed that their proposed approach exhibited better performance compared to state-of-the-art text-classification algorithms. Using text sequence relations in sentences, natural language processing technologies can improve the recognition rate of OCR systems. [PERSON] et al. [15] created a CNN model using a temporal classification loss function. They used dropout after each max-pooling layer to reduce overfitting. They achieved a character error rate of 6.81% on the ICDAR 2013 competition dataset.