# Towards Automatic License Plate Recognition in Challenging Conditions
## 2 Related Work  
This section details recent related works on license plate recognition. In [11], a region-based license plate detection method is discussed that initially shifts mean to filter and segment a color vehicle image to get candidate regions. These candidate regions are then analyzed to decide whether a candidate region contains a license plate. Since this method focuses on regions, so it is more robust to interference characters. In [12], the proposed method uses the YOLOv2 to detect vehicles. This work uses a CNN-based method that they refer to as WPOD-NET for LP detection. Meanwhile, a modified YOLO architecture recognizes the LP characters. However, this work also uses character segmentation, which makes this method a bit more complex than the compared methods. In [13], an improved YOLO architecture is deployed for character recognition. This work is tested on the SSIG dataset, which has 2000 images. In [14], a technique is developed that customizes the YOLO network to detect LPs from images that are captured in different conditions, such as different weathers, varying lighting, and other factors. The authors conclude that the YOLO can strike a balance between precision and recall. However, the YOLO is not suitable for detecting angular or small objects. Therefore, its performance in scenarios where the vehicle is far away from the camera needs to be further checked. In [15], a framework to detect and recognize license plates is discussed for complex scenes, which is based on mask region convolutional neural networks. The evaluation of this framework is further enhanced on four publicly available datasets for different countries. Moreover, this method is tested on diverse range of images, which are captured from multiple scenes, such as varying orientations, poor image quality, blurred images, and complex backgrounds. In [16], a convolutional filter of size 3 \(\times\) 3 is used in deep networks to analyze the increasing depth of the architecture by using 16 to 19 layers to process the 24 \(\times\) 24 pixel colored image. This work also introduces a pre-processing step by subtracting the average RGB value from each individual pixel. This paper reports significant improvements to ConvNets in the realm of image recognition as a result. In addition, this work also uses a large number of 3 \(\times\) 3 convolutional filters that fit well on the investigated datasets only. In [17], initially, candidate regions are selected through a sparse network using winnows classification, followed by filtration through CNN. An interesting novelty introduced in this work is the minimization of training and target domains in an unsupervised manner. However, this work also considers artificially generated synthetic LP images. In [18], the developed method utilizes thin-plate spline transformation and adaptively rectifies a textual LP image. Moreover, a recognition model predicts a character sequence immediately from the rectified image. This work only considers qualitative results on several images. In [19], a 2D attention-based encoder-decoder architecture is developed. This method extracts features by applying the ResNet CNN architecture. The 2D model introduced is capable of accommodating text with different layouts, arbitrary shapes, and different angles. Their reported results are encouraging, and their development reduces data bias and increases model generalization capacity. This method is simple; however, its generalization to standard datasets has not been explored. In a previous study [20], the authors used CycleWGAN to create LP images to improve the performance of recognition. Their work simultaneously generates images of different conditions. Meanwhile, a modified version of the CTC is used to recognize the LP. Their work simultaneously generates images of different conditions. Meanwhile, a modified version of the CTC is used to recognize the LP. In [21], an end-to-end irregular LPR (EILPR) is proposed using plate-level annotations during training.