## 4 Experiments
### Experiments on COFIE  
To investigate the impact of learning rates on model training, we conducted experiments with multiple different learning rates. In the preliminary experiments, we identified that learning rates in the range of \(1\times 10^{-5}\) to \(1\times 10^{-4}\) yielded good results, and the model stabilized within 30 epochs. We set the input resolution to 960 \(\times\) 1280, and selected \(F1\) as the evaluation metric. The model was trained for 30 epochs with varying learning rates (\(1\times 10^{-5}\), \(2\times 10^{-5}\), \(3\times 10^{-5}\), \(5\times 10^{-5}\), \(8\times 10^{-5}\)). Simultaneously, we recorded the \(F1\) obtained from the model on the test set and presented the experimental results clearly in Figure 6.  
In the experiment on learning rates, we observed that higher learning rates could achieve relatively high \(F1\) in the early stages of training. However, during the final convergence phase, models with these higher learning rates failed to achieve the optimal \(F1\). Instead, lower learning rates, which exhibited slower growth in the early stages, performed better during the final convergence. It is worth noting that when the learning rate was set to \(8\times 10^{-5}\), the \(F1\) significantly decreased, indicating that larger learning rates are not advisable. According to our experimental results, setting the learning rate to \(1\times 10^{-5}\) or \(2\times 10^{-5}\) yields the best performance. To explore the impact of different input resolutions on the final results, we conducted experiment with five different input resolutions (1280 \(\times\) 960, 960 \(\times\) 1280, 960 \(\times\) 960, 640 \(\times\) 640, and 320 \(\times\) 320), and we set the learning rate to \(2\times 10^{-5}\). While training, we recorded the \(F1\) obtained from the model on the test set. The detailed experimental results are presented in Figures 7 and 8, which illustrate instances of our text extraction. In the experiment on input resolutions, we explored five different resolution settings (1280 \(\times\) 960, 960 \(\times\) 1280, 960 \(\times\) 960, 640 \(\times\) 640, and 320 \(\times\) 320). From the experimental results, we clearly observed that larger resolutions lead to better \(F1\). It is worth noting that when the resolution was set to 640 \(\times\) 640, \(F1\) did not exhibit a significant decrease, while using 320 \(\times\) 320 led to a noticeable drop in \(F1\) due to substantial image distortion, making it challenging for the model to capture sufficient features. Therefore, it is advisable to choose a resolution greater than 960 based on the aspect ratio of the dataset images to achieve better results. Figure 6: Results of different learning rates. Figure 7: Results of different input resolutions. After conducting experiments on learning rates and input resolutions, we proceeded with comparative experiment between the Co-G-Ma feature enhancement module and other modules. In this experiment, we set the learning rate to \(2\times 10^{-5}\) and the input resolution to 960 \(\times\) 1280. After 30 epochs of training, we recorded the final \(P\), \(R\), and \(F1\) obtained from model trained with different modules on the test set. The experimental results are presented in Table 3.  
Due to the difficulty in collecting courier order images, we conducted experiments on a small-scale dataset. In the training set of the COFIE dataset, we selected \(1/2\) and \(1/5\) samples of COFIE for training. Due to the reduction in training data, we correspondingly increased the number of training epochs to 60 and 150. In this experiment, we used a learning rate of \(2\times 10^{-5}\) and set the input resolution to 960 \(\times\) 1280. We recorded the \(P\), \(R\) and \(F1\) obtained from model trained with different modules on the test set. Specific experimental results can be found in Tables 4 and 5. In the comparative experiments with other modules, our proposed Co-G-Ma module demonstrated outstanding performance.