# An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection
## 4 Result and Discussion
### Experiment Results and Discussion  
As shown in Table 2, when the HLFE module was added, the high-level semantic information of the network was fully utilized, thereby reducing the phenomenon of missed detection of small-scale text instances and false detection caused by the influence of non-text regions in the model. Therefore, the recall and accuracy were improved to a certain extent. When the FPRE module was added, the low-level semantic information of the network could be better propagated and utilized so that the model could better locate the text location and text boundary, improving the accuracy of the model significantly. When the HLFE and FRPE modules were applied together, the feature extraction effect was further improved. When the detection was performed after processing the image using the multi-channel MSER, the model's recall was improved more significantly because a large number of non-text regions were filtered out, which effectively reduced the influence of non-text regions on the text regions and made the text features more evident, indicating the significance of the MSER method for image pre-processing. The final precision of the model was improved from 91.8% to 92.5%, the recall rate was improved from 82.1% to 86.8%, and the F-Score was improved from 86.7% to 89.5%. As shown in Figure 9, the proposed method in this paper had a better detection effect for regions with inconspicuous text features, and it worked better than the original DBNet model in terms of recall, precision, and F-Score. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Methods** & **Precision (\%)** & **Recall (\%)** & **F-Score (\%)** \\ \hline DBNet (baseline) & 91.8 & 82.1 & 86.7 \\ DBNet + HLFE & 91.6 & 83.5 & 87.3 \\ DBNet + FPRE & 92.1 & 83.3 & 87.5 \\ DBNet + HLFE + FPRE & 91.8 & 84.1 & 87.8 \\ DNNNet + HLFE + FPRE + MSER (ours) & 92.5 & 86.8 & 89.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of results of ablation experiments on the NSSS dataset. Figure 9: Comparison of visualization results of the method proposed in this paper and original DBNet model on natural scene street sign (NSSS) dataset: (**a**) is the result of the proposed method in this paper, and (**b**) is the result of the original DBNet model. As shown in Figure 12, the method proposed in this paper showed better performances than the Baseline on both the ICDAR2015, ICDAR2017-MLT, and NSSS datasets in that the trained F-score tended to be stable with the increase in the number of training epochs. The yellow and green curves represent the F-score curves of our proposed method and the Baseline of the ICDAR2015 dataset, the purple and cyan curves represent the F-score curves of the method proposed in this paper and the Baseline of the ICDAR2015 dataset, respectively, and the blue and red curves represent the F-score curves of our proposed method and the Baseline on the NSSS dataset, respectively. Figure 11: Comparison of visualization results between the method proposed in this paper and the original DBNet model on the ICDAR2017-MLT dataset: (**a**) is the result of the proposed method in this paper, and (**b**) is the result of the original DBNet model. Figure 12: F-score curves of Baseline and our method on ICDAR2015, ICDAR2017-MLT, and NSSS datasets. To verify the effectiveness of the methods proposed in this paper, the following methods were used for a comparative study: the EAST model proposed by [PERSON] et al. [21], the PixelLink model based on instance segmentation proposed by [PERSON] et al. [15], the rotation-sensitive regression detector (RRD) proposed by [PERSON] et al. [33], the short path network (SPN) proposed by [PERSON] et al. [34], the TextSnake-like detection method (TextSnake) for irregular text proposed by [PERSON] et al. [35] and a path aggregation network (PANet) [36] is based on an instance segmentation framework. The objective of these methods was consistent with the goal of the work presented in this paper, which was to detect text in natural scenes.