## 4 Experiment
### Experiment and Analysis  
The public dataset used in this study was the ICDAR2015 dataset, which contained 1500 images taken in natural environments containing text. There were 1000 images in the training set and 500 images in the test set. The input image size was uniformly scaled to the VGG16 network model trained on ImageNet, which was used as the pre-training model. Moreover, the batch size was set to 6, the initial learning rate was set to 0.001, and the learning rate was reduced by a multiplier of 0.1 for every 300 epochs, with a minimum learning rate of 0.00001, until the model performance no longer improved. This study's ablation experiments were trained and tested on the ICDAR2015 dataset. The effects of the three improvements on the EAST model proposed in this paper are shown in Table 1. The EAST model with VGG16 as the core network is used as the reference object, and the rest of the improved model's core network also uses VGG16. As can be seen from Table 2, changing the loss function to Focal loss rather than balanced cross-entropy improved the precision, recall, and F1 scores by enhancing the model's learning for hard, positive samples. By improving the shrinkage strategy of label generation, the model more accurately predicted the short edge of the bounding box; thus, the precision and F1 scores improved. After adding FEM, although the precision decreased to some extent, the recall was improved from 73.52% to 76.94% because of the increased ability to detect long text, and the final F1 score was improved from 79.95% to 81.03%, thereby improving the overall model performance. As seen from Table 1, the effects of this paper's proposed improvements to the EAST detector were verified. As shown in Table 3, the results of this paper's improved EAST model were compared with other models on the ICDAR2015 dataset. In terms of recall, the proposed model was inferior to the original EAST model using the deeper ResNet50 as the core network. However, owing to the proposed model's advantages in precision, the F1 score and overall performance were nevertheless better than those of the other models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline EAST + VGG16 & 80.50 & 72.80 & 76.40 \\ EAST + Focal loss & 82.28 & 73.56 & 77.68 \\ EAST + Focal loss + improved & 87.61 & 73.52 & 79.95 \\ SAST + Focal loss + improved & & & \\ shrinking algorithm + FEM & 85.59 & 76.94 & 81.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the effects of ablation experiments. Figure 9: Select pictures from the SSTD dataset. Figure 10 compares the detection effects of the EAST model with this paper's improved model on the ICDAR2015 dataset. It can be seen that the improved EAST model was more accurate in generating bounding boxes of long text compared with the original model. Table 4 and Figure 11 compare the detection effects of the original EAST model with this paper's improved model on the SSTD dataset. On the SSTD dataset, the improved EAST model saw higher scores in both precision and recall compared with the EAST model, increasing the F1 score from 78.84% to 81.48% and overall performance. The comparison in Figure 11 shows that the improved EAST model was indeed more accurate in predicting longer text regions compared to the EAST model, which verified the effectiveness of this paper's proposed EAST model improvements. At the same time, the performance of the model on the SSTD data set verifies that the model proposed in this paper can solve the three challenges proposed in the second section. Our model can detect images obtained from multiple shooting angles in a bright or dark environment. Images of different resolutions contain multiple types of text such as Chinese characters, English, numbers, and punctuation marks.