## 4 The Line Extraction Pipeline  
In order to make use of our proposed progressive full data convolutional neural networks, we developed a system for line extraction from anime-style illustrations. We had two concerns in our \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Kernel Size** & **Stride** & **Output Size** \\ \hline Input & - & - & \(4\times H\times W\) \\ Convolution & \(3\times 3\) & \(1\times 1\) & \(32\times H\times W\) \\ Convolution & \(5\times 5\) & \(2\times 2\) & \(64\times H/_{2}\times W/_{2}\) \\ Convolution & \(3\times 3\) & \(1\times 1\) & \(64\times H/_{2}\times W/_{2}\) \\ Convolution & \(5\times 5\) & \(2\times 2\) & \(128\times H/_{4}\times W/_{4}\) \\ Convolution & \(3\times 3\) & \(1\times 1\) & \(128\times H/_{4}\times W/_{4}\) \\ Convolution & \(5\times 5\) & \(2\times 2\) & \(256\times H/_{8}\times W/_{8}\) \\ Convolution & \(3\times 3\) & \(1\times 1\) & \(256\times H/_{8}\times W/_{8}\) \\ Convolution & \(3\times 3\) & \(1\times 1\) & \(512\times H/_{8}\times W/_{8}\) \\ Convolution & \(3\times 3\) & \(1\times 1\) & \(768\times H/_{8}\times W/_{8}\) \\ Convolution & \(3\times 3\) & \(1\times 1\) & \(1024\times H/_{8}\times W/_{8}\) \\ Convolution & \(3\times 3\) & \(1\times 1\) & \(1280\times H/_{8}\times W/_{8}\) \\ De-convolution & \(4\times 4\) & \(2\times 2\) & \(128\times H/_{4}\times W/_{4}\) \\ De-convolution & \(4\times 4\) & \(2\times 2\) & \(64\times H/_{2}\times W/_{2}\) \\ De-convolution & \(4\times 4\) & \(2\times 2\) & \(32\times H\times W\) \\ Convolution & \(3\times 3\) & \(1\times 1\) & \(32\times H\times W\) \\ Convolution & \(3\times 3\) & \(1\times 1\) & \(32\times H\times W\) \\ Convolution & \(3\times 3\) & \(1\times 1\) & \(32\times H\times W\) \\ Output & \(3\times 3\) & \(1\times 1\) & \(4\times H\times W\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of layer parameters. Figure 4: Different designs of neural network structures: (**a**) traditional neural network; (**b**) full data neural network; (**c**) progressive full data neural network. system design: a standardized dataset and user involvement. These concerns were considered in our implementation. Standardized dataset: Illustrations are industrial or commercial assets, so they cannot be used freely. Under such circumstances, researchers need to create a dataset by themselves or collect data that are already available on the Internet. There is no doubt that search engines with powerful spiders or public photo galleries are the most powerful collecting channels, but the direct use of these data often fails to achieve good results because there is no uniform standard for training data; such as the background of line drawings, where white background and transparent background have two different standards. If both of these are reasonable outputs in the process of extracting line drawings, it is clearly not reasonable in the strict definition. In this paper, we adopted the standard that line drawings must have a transparent background. The pupils of the characters in the illustrations were sometimes completely blacked out, and we chose to ignore this kind of ground truth area. In order to obtain correct outputs, we still needed to clear the colored areas in the pupils while preserving the lines. User involvement: In art creation, the user's involvement is very important. Usually, neural networks can only react to user-entered training data. Therefore, the neural network needs to learn the stages of the drawing process. First of all, we should do some preliminary painter user study and pay attention to the painter user feedback data to ensure that painter users' knowledge can guide the model training direction. The second is to ensure the tool is easy for use. It is better if users do not need to make any settings at first and can rely on a fully automatic method to generate preliminary results. If the users have more detailed requirements, then they may configure the parameters to obtain semi-automatic output results. The efficiency of training and production are also important.