## 2 Model Building
### The Feature Extractor  
CNN have led to a series of breakthroughs for image classification [25; 26; 27], due to the characteristic of spatial subsampling, shared weights, and local receptive fields. Plain convolutional layers generally have pooling layers or multi-step convolution operations, which will subsample the feature map and may cause the loss of small object features, resulting in bad recognition performance of small characters such as \"\(\cdots\)\" \(\cdots\)\" and so on. Motivated by the super performance of the residual network (ResNet) [28], in this paper we involve shortcut connection strategy into traditional convolutional structure to form a feature extractor to extract efficient features. The core idea of ResNet is to introduce a shortcut connection in the network that can skip one or more layers and add the input of the upper layer directly to the output of the bottom layer. Figure 2 shows the shortcut connection structure in a residual block. Consider a reading string image \(I\) that is passed through a plain convolutional network with convolutional layers, each layer \(\zeta\) conducts a non-linear transformation \(F_{\zeta}(\bullet)\). For traditional plain network, the output of \(\zeta^{th}\) convolutional layer is:  
\[I_{\zeta}=F_{\zeta}\big{(}I_{\zeta-1}\big{)}, \tag{1}\]  
where \(I_{\zeta-1}\) is the output of the \(\ \left(\zeta-1\right)^{th}\) layer. For residual convolutional network, the corresponding \(I_{\zeta}\) is:  
\[I_{\zeta}=F_{\zeta}\big{[}F_{\zeta-1}\big{(}I_{\zeta-2}\big{)}+I_{\zeta-2}, \tag{2}\]  
if there is a residual connection between \(\left(\zeta-2\right)^{th}\) and \(\ \zeta^{th}\) layers, where \(I_{\zeta-2}\) is the output of the \(\left(\zeta-2\right)^{th}\) layer. Figure 2 shows the shortcut connection structure in a residual block.  
Figure 2: The residual block structure [28].  
This kind of connection can increase the information transmission between adjacent network layers, which can better fit high-dimensional classification functions and greatly enable the backbone network to learn more complex and accurate feature representation. In practice, the non-linear function \(F_{\zeta}(\bullet)\) is always composed of batch normalization [29], ReLU [30] and a convolution layer. In addition, the number of the shortcut connection layers can be flexibly chosen according to the specific task. In general, the feature maps \(X\) produced by the feature extractor in our study can be represented as:  
\[X=F_{\theta_{1}}(I), \tag{3}\]  
where \(\theta_{1}\) is the parameters of the feature extractor and \(F_{\theta_{1}}(\bullet)\) denotes the designed extractor, which is a CNN-based structure with shortcut connections to obtain high-level feature representation of multi-type characters. Before feeding into the sequence modeling module, the feature map \(X\) is converted into a feature sequence. Assuming that the length of the sequence output from the feature extractor is \(T\), it can be denoted as:  
\[X=\big{\{}x_{1},\dots,x_{j},\dots,x_{T}\big{\}}, \tag{4}\]  
where \(x_{j}(j=1,2,\dots,T)\) is a vector, in the same order to the corresponding local rectangle region of the raw image from left to right.