## 4 Results
### The Results of CCRNet on Chinese Characters  
We collected the dataset for Chinese character recognition. There were 5333 images of 18 classes, in which there were 14 Chinese characters and 4 English letters. 4799 images were selected as the set, and 534 images belonged to the testing set. We added ResNet18, ResNet34, and ResNet50 as the backbone of CCRNet. Epoch was set to 100 and the batch size was 16. Adam optimizer was also used to optimize the network and the initial learning rate was 0.0001. The loss of ResNet-based models is presented in Figure 5(a) on the training set. We can see that the convergence rate of ResNet18 was faster than ResNet34 and ResNet50. The performance of ResNet50 was better than ResNet34. Meanwhile, the accuracy of ResNet-based models is presented in Figure 5(b) on the training set. With the increasing of epoch, the accuracy of these three models is becoming better. After 60 epochs, the trend is becoming more and more flat. These three models performed significantly well in character recognition.  
As seen in Table 3, we trained the CCRNet model based on ResNet18, ResNet34, and ResNet50 for the testing set. The accuracy was over 99%. On the training set, the accuracy of CCRNet_ResNet18 exceeded 0.01% and 0.08% over CCRNet_ResNet34 and CCRNet_ResNet50. On the testing set, the accuracy of CCRNet_ResNet34 exceeded 0.18% over CCRNet_ResNet18 and CCRNet_ResNet50. The average accuracy of three models was over 99.2%, which demonstrates that ResNet-based models performed significantly well on Chinese character recognition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **Number** & **Precision** & **Recall Rate** & **mAP** \\ \hline \multirow{3}{*}{YOLOv5} & Training set & 568 & 99.60\% & 99.46\% & 99.44\% \\  & Testing set & 79 & 98.49\% & 98.81\% & 98.85\% \\ \cline{1-1}  & Validating set & 142 & 96.55\% & 95.40\% & 97.80\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of YOLOv5 for object detection on the training set, testing set, and validating set.  
Figure 6: The loss and accuracy comparison of character recognition on ResNet-based models. (**a**) The loss comparison. (**b**) The accuracy comparison.  
In addition, we compared the accuracy of Chinese characters and English letters, and the results can be seen in Table 4. We can see that the accuracy of English letters is 100% and better than Chinese characters. CCRNet_ResNet34 achieves 99.25% of accuracy and is 0.26% higher than CCRNet_ResNet18 and CCRNet_ResNet50.  
In Figure 7, the accuracy distribution of each character on CCRNet_ResNet18, CCRNet_ResNet34, and CCRNet_ResNet50 is presented, in which the value on the diagonal represents the number of each character. We can find that four characters are recognized to the error class on CCRNet_ResNet18 and CCRNet_ResNet50. Only three characters are recognized to the error class on ResNet34. Considering the performance and complexity, we used CCRNet_ResNet18 as the final backbone of CCRNet.  
The detection and recognition of Chinese characters is shown in Figure 8, in which all objects are precisely detected, and the confidence score is close to 1. In each image, there are different shapes of objects with different classes. All characters are fully recognized and located. From the experimental results, we demonstrated the significance of YOLOv5 and CCRNet on character detection and recognition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Chinese Character Accuracy** & **English Letter Accuracy** \\ \hline CCRNet\_ResNet18 & 98.99\% & 100\% \\ CCRNet\_ResNet34 & 99.25\% & 100\% \\ CCRNet\_ResNet50 & 98.99\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracy of CCRNet on Chinese characters and English letters.  
Figure 7: _Cont._