## 4 Segmentation-Free End-to-End LPCR By Object Detector  
This section discusses how the end-to-end object detector can be used as a segmentation-free end-to-end LPCR method. [PERSON] et al. [48] proposed a novel state-of-the-art real-time object detector (YOLOv2) that can detect 9000 different categories. While existing CNN-based object detection models such as Faster R-CNN [49] perform region proposals first and then classify each set of boundary boxes, the YOLOv2 detector considers region proposals and class probabilities as one regression problem and simultaneously performs the location and classification of objects in a single CNN. Using this idea  
Figure 4: Training of GAN-based image-to-image translation networks.  
Figure 5: Generation of LP images using LP-GAN.  
of the YOLOv2 detector, it only needs to detect 45 character classes in the LP images. Accordingly, the whole LPCR process is carried out at once.  
In practical ALPR systems, it is necessary to minimize the processing time and the size of required GPU memory in the LPCR stage, because the system must carry out other important processes such as acquiring images, storing recognition results, and communicating with a remote server. In addition, when considering the cost, a system with a powerful but expensive GPU processor may not always be available to run on the ALPR system, so the architecture needs to be as cost-effective and lightweight as possible. To improve these problems, we propose a modified YOLOv2 model with half of the CNN-layer filters in the YOLOv2 detector architecture proposed by [PERSON] et al. [48] as the LPCR method. The architecture and structure of the proposed LPCR module (i.e., modified YOLOv2) is given in Table 2 and Figure 6, respectively. The modified YOLOv2 model outputs a 13 \(\times\) 13 \(\times\) 512 feature map from a 416 \(\times\) 416 pixel 3-channel image through five steps of convolutional and maxpool layers (Figure 6B). Next, the 26 \(\times\) 26 \(\times\) 256 feature map layers pass through and are reshaped into 13 \(\times\) 13 \(\times\) 128 feature map layers, as shown in Figure 6A, and become 13 \(\times\) 13 \(\times\) 640 feature map layers by concatenation with the output from Figure 6B. In the last step, 13 \(\times\) 13 \(\times\) 250 layers are output for the location and classification of the 45 character classes that is needed to detect (i.e., the prediction of 5 boxes with 5 coordinates each and 45 classes per box = 5 \(\times\) (5 + 45)). If sufficient amount of training data were provided, the proposed YOLOv2 detector as a segmentation-free end-to-end LPCR method could achieve high performance for detecting 10 number classes and 35 character classes, which was experimentally confirmed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Layer Type** & **Filters** & **Size / Stride** & **Output** \\ \hline
0 & Convolutional & 16 & 3 \(\times\) 3 & 416 \(\times\) 416 \(\times\) 16 \\
1 & Maxpool & - & 2 \(\times\) 2 / 2 & 208 \(\times\) 208 \(\times\) 16 \\
2 & Convolutional & 32 & 3 \(\times\) 3 & 208 \(\times\) 208 \(\times\) 32 \\
3 & Maxpool & - & 2 \(\times\) 2 / 2 & 104 \(\times\) 104 \(\times\) 32 \\
4 & Convolutional & 64 & 3 \(\times\) 3 & 104 \(\times\) 104 \(\times\) 64 \\
5 & Convolutional & 32 & 1 \(\times\) 1 & 104 \(\times\) 104 \(\times\) 32 \\
6 & Convolutional & 64 & 3 \(\times