# AT-Text: Assembling Text Components for Efficient Dense Scene Text Detection
## 4 Experiments
### Comparison Results  
The comparison results of the proposed approach with existing methods on the ICDAR2013 dataset are reported in Table 3.  
We achieve the precision, recall, and F-measure values of 87.93%, 80.56%, and 84.07% on the ICDAR2013 dataset, respectively, which shows less margin than most other MSERs-based works [1; 16; 36; 37]. The advancement is significant in terms of recall, which is about 7% better than work with the same precision in [PERSON] et al. [1]. In Table 3, the other works had a large margin between the precision and recall metrics. Particularly, the recall is less than the precision of about 10%, resulting in a low F-measure value. Text detection with a low recall probably brings extra difficulties in the subsequent text recognition procedure. Figure 6 displays several text masks and text detection results on the ICDAR2013 dataset. Despite the existence of blur, low contrast, and text-liking disturbance, the text masks efficiently identify text locations. Table 4 shows the performance of the AT-text on the MSRA-TD500 dataset. AT-text achieves a significant improvement in terms of recall compared with other methods. There is also an obvious margin between the precision and recall in the existing works [5; 12; 13; 25; 38]. This phenomenon might be explained by the inherent limitation of heuristic rules for detecting a variety of text patterns and oriented text. AT-text gains improvement from two factors. One is that the accurate text segmentation mask supplies rich meaningful supervision to robustly identify Only-Text components, and then avoids designing heuristic rules for filtering out false-positive components. The other factor is that the MSERs algorithm could efficiently detect connected components with detailed information in the relatively simple background, which assists text line construction in a multi-oriented and multi-language task. Some typical results are demonstrated in Figure 7. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strategies** & **Precision** & **Recall** & **F-Measure** \\ \hline Single + \(\Delta\)(10) & 64.03 & 63.36 & 63.70 \\ Single + \(\Delta\)(1) & 74.89 & 78.04 & 76.44 \\ Single + \(\Delta\)(1,5,10) & 75.06 & 78.32 & 76.66 \\ Fuse + \(\Delta\)(5) & 82.24 & 75.48 & 78.72 \\ Fuse + \(\Delta\)(1) & 86.19 & 80.24 & 83.10 \\ Fuse + \(\Delta\)(1,5,10) & 87.93 & 80.56 & 84.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performances of various networks and different delta (\(\Delta\)) on the ICDAR2013 dataset (%). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **F-Measure** \\ \hline The proposed & 87.93 & 80.56 & 84.07 \\ [PERSON] et al. [16] & 89.50 & 77.63 & 83.14 \\ [PERSON] et al. [36] & 85.15 & 75.89 & 80.25 \\ [PERSON] et al. [1] & 88.00 & 74.00 & 80.00 \\ [PERSON] et al. [37] & 85.80 & 74.34 & 79.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different text detection methods’ performance on the ICDAR2013 dataset (%). Figure 7 displays several results of produced text masks and text lines grouped with parsimonious components in MSTD-500 datasets, which demonstrate that the proposed method is highly robust to various text patterns and multi-language texts. Our method suppresses the cluttered background and focuses on text regions to supply guidance for correctly predicting multi-scale and multi-language word/text lines. Figure 8 shows produced the text masks, MSERs-generated components, and final text lines in the three challenging images. The top raw is an example of multi-oriented text detection. The middle raw is a result of the low-quality image with the disturbance of text-like outliers. The bottom is a text line with large character spacing. In the three challenging cases, the text masks supply accurate text region locations to correctly identify positive text components, benefiting from the proposed segmentation model encoding powerful semantic features for multi-oriented text, low-quality text,