## 3 Improving the East Model
### Improving the Network Structure of the EAST Model  
The network structure of the EAST model can be roughly divided into three parts [18]: feature extractor stem, feature-merging branch, and output layer. The EAST model's network structure is shown in Figure 4a. The EAST detector uses PVANet as its backbone network to extract features and obtains four levels of feature maps, f1, f2, f3, and f4, with sizes of 1/4, 1/8, 1/16, and 1/32 of the input image, respectively. Larger feature maps have shallower depths and smaller receptive fields, and contain more details, and are used to extract features of small targets; smaller feature maps have larger depths and receptive fields, contain more high-level semantic information, and are used to extract features of large targets. The feature maps f1, f2, f3, and f4 are then merged using the U-shape idea to obtain a feature map that contains both high-level and low-level information. This feature map is then fed into the output layer, and, by mapping across different convolutional layers, the network obtains a score map and a multi-channel geometry map. The geometry feature map contains two parts: RBOX geometry, with axis-aligned bounding box (AABB) information and rotation angle information, and QUAD geometry, with eight channels of coordinate shift information. The classic VGG16 network proposed by [PERSON] et al. [26] is used for the feature extraction stem of the network, removing the final fully connected layer and only preserving the front portion of the full convolutional layer to constitute the core network. However, the theoretical receptive field of the VGG16 network is only 212, and the actual receptive field is smaller, hampering feature extraction for large-scale text regions with input size of 512 \(\times\) 512 pixels. Therefore, this paper proposes an FEM to increase the receptive field of the model. The network structure of the improved EAST model is shown in Figure 4b. The FEM is inspired by dilated convolution, proposed by [PERSON] et al. [27], and the Inception structure, proposed by [PERSON] et al. [28; 29]. A brief description is given below. As shown in Figure 5a is a \(3\times 3\) convolution with a dilation factor of 1, with a receptive field per element of 3 \(\times\) 3, and Figure 5b is a \(3\times 3\) convolution layer with a dilation factor of 2, with a receptive field per element of 7 \(\times\) 7. Dilated convolution can increase the receptive field exponentially without exponentially increasing the number of parameters. [PERSON] et al. [28] proposed a structure that aggregates multiple features at different scales, merges them, and then outputs them, namely the Inception V1 module, which can widen the network and control parameter numbers when extracting high-dimensional features. In 2016, [PERSON] et al. [29] improved on the previous model by proposing that a \(5\times 5\) convolution can be decomposed into two 3 \(\times\) 3 convolutions, and an \(n\times n\) convolution can be decomposed into a combination of a \(1\times n\) convolution and an \(n\times 1\) convolution to further reduce parameters, such that a convolution kernel's number of parameters can be reduced from \(n\times n\) to \(2n\). Figure 3: Example of problems with the original bounding box shrinking algorithm. Figure 4: The network structure of the EAST model and improved EAST model. The structure of the FEM designed in this study is depicted in Figure 6. Inspired by the Inception module, the FEM extracts features using a total of four sizes of convolution kernels, 1 \(\times\) 1, 3 \(\times\) 3, 5 \(\times\) 5, and 7 \(\times\) 7, and then merges the extracted features. Considering the reduced number of parameters, the convolution kernels of sizes 3 \(\times\) 3, 5 \(\times\) 5, and 7 \(\times\) 7 are split; that is, the convolution kernel of 3 \(\times\) 3 can be split into a combination of 1 \(\times\) 3 and 3 \(\times\) 1.