## 4 Experiment
### Experiment and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline CTPN + VGG16 & 74.20 & 51.60 & 60.90 \\ Seglink + VGG16 & 73.10 & 76.80 & 75.00 \\ WordSup & 77.03 & 79.33 & 78.16 \\ [PERSON] et al. [31] & 72.26 & 58.69 & 64.77 \\ EAST + VGG16 & 80.50 & 72.80 & 76.40 \\ EAST + ResNet50 & 77.32 & 81.66 & 79.43 \\ EAST + PAVNET2x & 83.60 & 73.50 & 78.20 \\ EAST + PAVNET2x MS & 84.64 & 77.23 & 80.77 \\ STN-OCR [32] & 78.53 & 65.20 & 71.86 \\ Poly-FRCNN-3 [33] & 80.00 & 66.00 & 73.00 \\ RFPN-4s [34] & 85.10 & 76.80 & 80.80 \\ Ours & 85.59 & 76.94 & 81.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparing detection effects of the improved EAST model with other models on the ICDAR2015 dataset. Figure 10: Comparison of detection effect between the EAST model and improved EAST model on the ICDAR2015 dataset. To address the EAST model's deficiencies in detecting long text regions because of small receptive fields, three improvements were proposed: altering the shrinkage calculation during label generation, adding a feature enhancement module (FEM), and changing the loss function approach to Focal loss. Compared with the original EAST model, the improved model increased the F1 score from 76.40 to 81.03 on the ICDAR2015 dataset, and from 78.84 to 81.48 on the SSTD dataset. The improved EAST model's detection effects for long text regions were also enhanced. Ultimately, this paper proposed an improved EAST model with the better overall performance for text region detection in street sign scenes. In future research, based on the work we did before, on the one hand, we will expand the data set with pictures containing Chinese characters of minority nationalities, so that the model can detect more kinds of characters. On the other hand, we will tackle text recognition from regions with high tilt angles and perspective distortion, as well as street sign scenes that contain Chinese characters, English letters, numbers, and punctuation marks. Then we will propose a model that recognizes the text detected by the improved EAST model, to propose a combined model that can fully detect and recognize street sign text from end to end. In addition, we will conduct research on the application of our work in the field of assisted navigation for autonomous driving and intelligent translation. The authors' contributions are summarized below. [PERSON], [PERSON] and [PERSON] made substantial contributions to the conception and design. [PERSON] and [PERSON] were involved in drafting the manuscript. [PERSON], [PERSON] and [PERSON] acquired data and analysis and conducted the interpretation of the data. The critically important intellectual content of this manuscript was revised by [PERSON]. All authors have read and agreed to the published version of the manuscript. The authors would like to thank the anonymous reviewers and the editors for all the helpful suggestions. This research was funded by the National Social Science Fund of China, grant number (20 BGL141). Not applicable. This study is only based on theoretical basic research. It does not involve human subjects. The data used to support the findings of this study are available from the corresponding author upon request. The authors declare no conflict of interest.