## 4 The Line Extraction Pipeline
We tried to use simpler structures and smaller resolution training data, and users only needed to provide around 30 training source images to our system to complete the line drawing extraction model training. Finally, we tried to avoid extra pre-processing or post-processing of the data, such as vectorizing the results, which may remove the details together while de-noising. In order to maintain the balance between training data size and neural network structure complexity, our model adopted a strategy of increasing neural network complexity in exchange for reducing training data size. Our system had three key points: the design of the neural network system, the preparation of training data, and the determination of both subjective and objective evaluation criteria. The pipeline of our system is depicted in Figure 5.  
Here, we briefly explain the different blocks and factors of the pipeline as follows:  
Data collection: The initial work of the pipeline was data collection. This part of the work is often the least creative, but it is also the most labor intensive. We could collect data through the Internet or manually create a corresponding database for management. Then, we needed to conduct data analysis and select the data that were suitable for training. Using raw data for training would cause noise problems and could not achieve high quality output. After the data analysis, we could standardize  
Figure 5: The pipeline of line extraction. the selected data to reduce the output noise and use less training data. Then, we could ensure the uniqueness of the training direction. The neural network of the training system adopted our proposed progressive full data convolutional neural network. According to different training datasets, we could complete the extraction of line drawings for different types of images. Here, we mainly chose anime-style illustration data for testing, because the patterns of anime-style illustrations are similar. General users could extract lines by using the line extracting model for getting outputs automatically. Usually, users did not need to train the model by themselves. If there were specific requirements, then users could re-select the training dataset and re-train the corresponding model. Users only needed to adjust the output of the existing model and input the adjusted result into the data collector to regenerate the training dataset. Then, an updated model could be generated by re-training. The training data were collected from the Internet or created by ourselves. It would be better if we could directly obtain the corresponding line drawings from artists. Completed colorized illustrations were used as input data and the corresponding line drawing images as output data. The data were selected manually so the quantity may be not enough. By using noise generation, clipping, and scaling methods, we increased the amount of input images. The training input image size was 128 \(\times\) 128 pixels. Patterns' collection: Usually, the size of a source illustration is much larger than the training input image size. Thus, we needed to find the patterns of source illustrations instead of submitting full illustration images to the training system in order to save computational resources. We had 20 pairs of training input pattern images. The quantity of the patterns was still not enough for covering all drawing styles, but it was good enough to extract line drawings from some illustrations. We used pattern images to describe the data features in a simple way, and our convolutional neural network could extend these features. The sample images of the training data generated by using the following guidelines are shown in Figure 6.  
1. The first consideration was whether the background was transparent or white. We recommend using a background transparent ground truth, because in some images, the white color may be the line color. Only the alpha value can directly provide a certain value of a transparent background. 2. The second consideration was the thickness of the line.