## 5 Experimental Results  
We implemented our network with Chainer, and we trained and tested our model on a machine with Intel E3 1230 v3 CPU and Nvidia GTX 1080 Ti GPU. There were finally around 10 pairs of source images and 10,000 pairs of input images generated from the source images by clipping and scaling operations. The training step took about 93 h for 1,000,000 iterations with a batch size of 16. The database size was 10,000; thus, there were about 1600 epochs.  
The results in Figure 7 show the advantages of our model. The traditional model similar to U-Net with GAN was confused about how to handle the lines, so the lines were not as clear as ours. The image in Column (c) in the second line shows that the background removal was not completed, so it had more noise in the background than the image in Column (d), which was generated by our model.  
Figure 7: Comparisons of experimental results: Images of Column (**a**) are input and images of Column (**b**) are ground truth; Images of Column (**c**) and images of Column (**d**) were generated by different models; both of these models’ loss values were converged for rough comparison, and the same parameters and training database were used. We can see clearly that our model obtained better results than those of the models based on the traditional U-Net structure.  
From the results in Figure 8, we can see that the outputs in the control group had more faults than the outputs generated by our model. Our lines were clear.er Our model and the models we compared had the same training database, but had different understanding of the lines. The results showed that our model correctly removed the dark area and reserved the lines around it. The model recognized by itself that we only needed the lines, but the model without a progressive structure may regard the black as some kind of very bold lines.  
There are more results in Figure 9. These outputs showed that most of the time, our model worked well. The traditional models also reached the goal of line extraction, but the results were not good enough. There were mainly three points by which our model worked better than the traditional U-Net models:  
1. Cleaner lines.
2. Prediction based on pixel level and global level views.
3. Better background removal.  
Figure 8: Experimental results: the original images are shown in the first column, the images generated by a model similar to U-Net with GAN ([PERSON] et al. [9]) are shown in the second column, and the images generated by our model are shown in the third column. Details are highlighted and compared in the two small images below the results, and the right one of the two was generated by our model.  
Based on the feedback from users, we obtained the following evaluation. Our system had a greater precision than those of other recent systems. The basic reason was that the system that integrated with our model retained the details of the data as much as possible, while other systems preferred fuzzy processing. The fully automatic mode was more convenient for users who did not understand how a neural network works. The cumbersome training data preparation and the working principle of the neural network are difficult for general users to understand, so it is not yet possible to achieve their needs through simple steps. For example, professionals have great interest in whether the system can be used to train high precision line corrections.