# A Two-Stage Automatic Container Code Recognition Method Considering Environmental Interference
## 4 Experiment
### Experiment of Box Number Character Recognition
Figure 13: Recognition accuracy of different characters. subsequently fed into C-DeepLabv3+ for character recognition. The resultant recognition rates and speeds are detailed in Table 8.  
We combine Tables 1 and 8 into the same table to compare the box number recognition accuracy and recognition speed for different documents, and the results are shown in Table 9.  
As can be seen from Table 9, our proposed ACCR, in terms of recognition success rate and recognition speed, outperforms all previous methods, reaching 99.51% and 115 ms, respectively. The ACCR methods proposed in the previous literature are either too slow or have insufficient success rates. The recognition success rate of the method proposed by [PERSON] et al. is the highest, reaching 99.3%, which is only slightly lower than that of our method, but its recognition speed is 15.6 times slower than our method, and it cannot realize real-time detection. [PERSON] et al. 's method is slightly faster than our method, but its recognition success rate is low, and its robustness is poor in complex environments, and the application at the dock gates is poor; [PERSON] et al. 's method has a more balanced recognition speed and success rate and also has a certain degree of resilience to the complex environments, but it is still not as excellent as our method. The other methods in the table are not good enough in both recognition success rate and recognition speed, and they will not be repeated. In summary, our method achieves the highest recognition success rate and speed, and it can accurately and quickly obtain recognition results in various complex scenes. Figure 14 shows some of the box number character recognition results. \begin{table}
\begin{tabular}{c c c} \hline
**Overall Success Rate (\%)** & **Recognition Speed (ms)** \\ \hline
99.51 & 115 \\ \hline \end{tabular}
\end{table} 
Table 8: Overall success rate and speed of the recognition algorithm. \begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & \begin{tabular}{c} **Number Area** \\ **Localization Method** \\ \end{tabular} & **Character Recognition** & **Success Rate (\%)** & **Recognition Speed (ms)** \\ \hline [PERSON] and [PERSON] 2013 [6] & \begin{tabular}{c} Edge-based and \\ sliding windows \\ \end{tabular} & BP Neural Network & 98.39\% & 240 ms \\ \hline [PERSON] et al., 2016 [16] & Based on appearance & SVM & 96.00\% & 580 ms \\ \hline [PERSON] et al., 2016 [17] & Edge-based & \begin{tabular}{c} LeNet-5 and Template \\ Matching Method \\ \end{tabular} & 94.70\% & 920 ms \\ \hline [PERSON] et al., 2018 [18] & \
\begin{tabular}{c} Grouping of connected \\ components \\ \end{tabular} & LeNet-5 & 95.41\% & 10,000 ms \\ \hline [PERSON] et al., 2018 [27] & MSER and CTPN & CRNN & 93\% & - \\ \hline [PERSON] et al., 2018 [7] & - & Width learning system & 97.85\% & 20 ms \\ \hline [PERSON] et al., 2020 [19] & MSER &
\begin{tabular}{c} LeNet-5 and template \\ matching method \\ \end{tabular} & 99.30\% & 1800 ms \\ \hline [PERSON] et al., 2020 [20] & ResNet and U-net & CRNN & 93.33\% & 885 ms \\ \hline Chun-ming 2020 [10] & SSD & SVM & 94.60\% & 630 ms \\ \hline [PERSON] et al., 2020 [22] & YOLOv3 & CRNN & 96\% & 159 ms \\ \hline [PERSON] et al., 2020 [22] & EAST & - & 96\% & 52 ms \\ \hline [PERSON] et al., 2021 [11] & DBNet & SAR & 95\% & 200 ms \\ \hline [PERSON] et al., 2022 [24] & Edge-based recognition & SVM & 97.30\% & 100 ms \\ \hline Ours & C-YOLO & C-Deeplabv3+ & 99.51\% & 115 ms \\ \hline \end{tabular}
\end{table} 
Table 9: Comparison of metrics for box number identification in different models.