# AT-Text: Assembling Text Components for Efficient Dense Scene Text Detection
## 3 The Proposed Method
### Segmentation Model  
The text encodes semantic information, which makes it different from background. In this paper, inspired by recently deep learning development, we adopt a segmentation model that particularly  
Figure 2: Comparison of the pipelines of the previous works and the proposed approach. The top row (**a**) is a common workflow of previous component-based methods. The bottom row (**b**) is the proposed framework.  
focuses on scene text to provide semantic mask of text regions. To realize this capability, the model is implemented with a fully convolutional network [31] architecture for pixel-wise text prediction.  
The details of the architecture are schematically illustrated in Figure 3. The VGG network is employed as a backbone to extract text features. Since the sizes of text regions vary tremendously, determining the existence of large texts would require features from the late-stage of the VGG network, while predicting accurate small text regions needing low-level information in early stages. The convolutional layers, followed by RELU (Rectified Linear Unit), are regularly divided into five stages by four MaxPooling layers. These convolutional layers with \(3\times 3\) convolutional kernel have 64, 128, 256, 512, and 512 kernel numbers, respectively (denoted as \(n\) in Figure 3).  
Different stages have the variable receptive fields and can capture meaningful multi-scale features. The skip connections with two convolutional layers are used to correspondingly combine coarse features and fine features from different stages. Particularly, we select the feature maps before pooling layers to reduce detail information loss. Then, we use \(1\times 1\) convolutional layers and deconvolution layers to up-sample previous coarse outputs. Finally, the \(1\times 1\) convolutional layer and the sigmoid layer are exploited to efficiently generate a pixel-level prediction. Each convolutional layer is followed by ReLU (Rectified Linear Unit) activation. More details of the network can be found in [PERSON] et al. [32]. The outputs of the segmentation model are discretized into a binary mask, where values above 0.5 are set to 1 and values below 0.5 are set to 0.  
We utilize the training set supplied by the corresponding datasets to finetune the pretrained model. Treated as the binary classification of text/non-text pixel, our method applies cross-entropy  
Figure 3: The architecture of our segmentation model. The final fusing feature map is in the same resolution as the input image and is converted to the text mask. We use \(n\) represents the number of convolutional kernels. The blue layers are Max-pooling, the orange are convolutional layer with \(3\times 3\) kernel size, the yellow are convolutional layer with \(1\times 1\) kernel size, the green is deconvolution layer, and the purple is sigmoid operation.  
loss formulated as Equation (1), where y text denotes pixel-level text label and p text represents the probability of the text pixel.  
\[\text{Loss}=-(\text{y\_text{text}}\text{text}\text{text}\text{text}\text{text} \text{text}\text{text}\text{text}\text{text}}\times\log(\text{p\_text{text}} \text{text}\text{text}\text{text}})+(1-\text{y\_text{text}}\text{text}\text{ text})\times\log(1-\text{p\_text{text}}\text{text}\text{text}\text{ text})). \tag{1}\]  
Figure 4 illustrates qualitative results of the produced text masks. It shows that the segmentation model can localize multi-oriented texts, the curved texts in cluttered background, and then provide holistic supervision for identifying Only-text components.