## 3 Methodology
### Baseline Model Pre-Training  
Using the different font generation, background simulation, and data augmentation processes illustrated in Sections 3.1 and 3.2, we expanded the dataset to generate a sufficient amount of training data, ultimately using up to more than 19.6 million images to accommodate the diversity of input image variations and strengthen the recognition capability of the CNN model. We were inspired by two studies in the field of handwritten Chinese character recognition (HCCR): HCCR-GoogLeNet [7] and MelnykNet [12]. To conduct a preliminary classification test, we used a portion of the real data gathered, a total of 66 different characteristics. We also used InceptionV4 [39] for comparison. As GoogLeNet and MelnykNet perform better, we chose these two models for investigation. In this study, we utilized and modified the GoogLeNet [5,7] and MelnykNet [12] models as a baseline and pre-trained them with the abovementioned synthetic Chinese characters dataset. According to [PERSON] et al. 's research [7], traditional, less demanding CNNs, offered for handwritten Chinese character recognition, were neither deep nor slender enough to extract appropriate features to achieve better classification performance. We did not consider using standard CNNs, such as AlexNet, because GoogLeNet has a deeper architecture and can be developed with fewer parameters to achieve superior performance. **GoogLeNet-GAP model. ** GoogLeNet [5] was proposed by Google in 2014 and won the ILSVRC competition. By adding different sizes of the kernel to extract the features of images at different scales, they also added \(1\times 1\) convolution to reduce the amount of calculation, and finally combined these features to form the Inception v1 block [5]. Combining multiple inception blocks can increase the diversity of the feature map and make the model converge more quickly. Since our goal is to classify large-scale categories of Chinese printed characters, to avoid overfitting caused by a large number of characters, we replaced the FC layer with a global average pooling (GAP) layer (as plotted in red-dashed line in Figure 9) to reduce the number of model parameters and improve its classification performance. Figure 9 shows the modified network architecture GoogLeNet-GAP. **MelnykNet-Res model. ** MelnykNet was originally proposed for offline handwritten Chinese character recognition [12]. In the study of [PERSON] et al., the handwritten character data were binary images and the background was clear [12] hence, the MelnykNet model only requires a few parameters and can be trained to perform well. However, as our input images are grayscale images with a noisy background, it was necessary to strengthen the feature extraction capabilities of the CNN model to achieve better recognition performance. According to [PERSON] et al. [40], the feature maps extracted by each convolutional layer differ; the higher the layer, the finer the feature maps, and vice versa. In the original architecture, the contour of the characters were extracted by the previous convolutional  
Figure 9: Modified GoogLeNet architecture: GoogLeNet-GAP model. layer, so we added a residual block [36] (as plotted in the red-dashed line in Figure 10) to the later layer to continue to extract the detailed features of the character. The modified MelnykNet model MelnykNet-Res is illustrated in Figure 10.  
In this study, we collected a test dataset containing 4944 samples of 294 Chinese characters cropped from real ID card images. Preliminary experiments were conducted using the above-mentioned GoogLeNet-GAP model and MelnykNet-Res model. We performed training using the synthetic training dataset and compared the recognition rates of the two models using a real testing dataset. Table 2 presents the performance of these two models, given the different numbers of training samples augmented for each character. As may be observed from Table 2, the modified GoogLeNet model performed better than the MelnykNet model. The highest recognition rate was 95.71%.