# AT-Text: Assembling Text Components for Efficient Dense Scene Text Detection
## 3 The Proposed Method
### Text Components Generation  
An example is shown in Figure 4, where the dense text instances have touching effects in the segmentation mask. Therefore, directly producing the text lines may be difficult or unreliable. We look for a bottom-up manner that is able to extract part of texts and distinguish boundaries using local characteristics of text instances. The MSERs algorithm extracts connected components that remain stable over a range of intensity thresholds. It has a high capability to generate components of various texts, so it has been widely adopted in bottom-up text detectors. However, the methods based on the MSERs algorithm are confronted with two problems. First, as long as a connected component is missed, it would be hard to recollect in the subsequent steps. Therefore, MSERs-based methods prefer to generate more candidate components with weak constraints. Second, the weak constraints might lead to producing more false text components--i.e., covering the texts and the large-area background regions simultaneously (named Text-Background components in this paper). We gather the statistics of MSERs-generated components in Figure 1, as shown in Table 1. Delta (\(\Delta\)) is the threshold measuring intensity contrast of the stable regions, which equals to 1, 5, 10 respectively. \"Total-Number\" represents the total number of generated connected components. \"Text-Background\" is the number of components that contain text regions and background simultaneously. The \"Non-Text\" can be obtained by calculating the numbers of these three columns. Additionally, we compute the ratio account for \"Total-Number\" for the clarity of comparisons.  
In Table 1, the total number is huge with either three deltas. Maximum is 31,494 when delta equals 1. Besides this, a smaller delta is able to obtain more components than a bigger one. Shown by Only-Text columns, there are tiny number of components are enclosed by the text regions. The ratio  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Delta (\(\Delta\))** & **Total-Number** & **Text-Background** & **Ratio of Text-Background** & **Only-Text** & **Ratio of Only-Text** \\ \hline \(\Delta=1\) & 31,494 & 6469 & 20.54\% & 458 & 1.45\% \\ \(\Delta=5\) & 23,702 & 4912 & 20.72\% & 311 & 1.32\% \\ \(\Delta=10\) & 13,299 & 3138 & 23.60\% & 233 & 1.75\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics of MSERs-generated components with different \(\Delta\) on a scene image of ICDAR2013 (International Conference on Document Analysis and Recognition).  
Figure 4: The text masks of scene images with cluttered background and multi-oriented texts.  
intuitively indicates this conclusion. When delta equals to 5, only 1.32% of components could be utilized to construct text lines. The large number of Non-Text and Text-Background brings great pressure to the classifier and post-processing in the previous works. [PERSON] et al. (2020) selected an average of 516 components to the CNN classifier in one image on ICDAR2011 dataset. In this paper, we further classify the Text-background, almost 20% components, as negative samples to be discarded directly. Moreover, for compensating the less Only-Text, we combine text components generated by the different values of delta in the phase of text line construction.  
Figure 5 shows samples of our positive text components. Particularly, the components of Chinese characters are not constrained with the entire character. Therefore, a character with un-connected strokes is extracted by several components to suit the multi-language text detection. Additionally, the proposed approach can deal with different scale text conveniently. Compared with the previous methods, we only provide parsimonious text components for subsequent steps.