# Towards Automatic License Plate Recognition in Challenging Conditions
## 2 Related Work
In the EILPR method, a coarse-to-fine approach is implemented that extracts the LP features for sequence recognition. This work assumes the fact that a LP may generate a perspective bias in the image; therefore, to cater to this fact, an automatic perspective alignment network (APAN) is introduced to extract the fine license plate features. To classify the international license plates, a location-aware 2D attention-based recognition network is used. In [22], a novel ALPR technique, which is referred to as VSNet, is developed. The VSNet contains two CNNs that are combined in a cascading manner. Meanwhile, an integration block is introduced that extracts the spatial features. With vertex supervisory information, authors develop a vertex-evaluation module in VertexNet such that a LP can be repaired as the input images of SCR-Net. A horizontal encoding algorithm is used in the SCR-Net to extract left-to-right features and then recognize a license plate. This work performs well on standard LPs. However, its generalization capability on tilting and rotating LPs has not been explored. Additionally, ALPRNet is developed to detect and recognize mixed-style license plates [23]. Two fully convolutional object detectors are used in the proposed ALPRNet to classify and recognize LPs. The proposed ALPRNet processes LP and character equally. In this work, object detectors output bound boxes of LPs along with corresponding labels without the application of the RNN branches of the OCR. This is because this is a single-stage network-based method. Therefore, its detection accuracy on challenging datasets has not been explored. In [24], image processing and OCR-based techniques are merged to recognize the LPs. The image processing methods utilize color conversion, [PERSON]'s thresholding, and noise removal. The OCR method uses template matching to predict the characters of LPs. The authors of this work have not examined the scalability of this method and have only used basic tools from signal and image processing. In [25], the proposed LPR method consists of three steps: LP detection, unified character recognition, and multinational LP layout detection. This work is primarily based on the YOLO networks. To extract the correct sequence, a layout detection scheme is introduced, which extracts the sequence of LP numbers from multinational LPs. This study is extensively tested on standard Koran and Taiwan LPs. In [26], the developed LPR method uses a joint combination of adaptive boosting and the LDA to extract features. The CNNC is then used to separate the LP region from irrelevant samples. This work is segmentation-free. However, its recognition capability on real-world images has not been explored. In [27], the developed algorithm uses a distinct, fine-tuned YOLO-v3 platform to extract LP characters from input images. During the training and testing stages, a wide range of LP images have been analyzed. However, this system is fully annotated and consumes over 100 ms to accomplish the task of LP recognition. In addition, an intriguing review article is released that summarizes the many approaches currently utilized to detect various objects [28]. In [29], researchers introduced a robust vehicle detection method using a multi-scale deep convolutional neural network. This work utilizes a standard Gaussian mixture probability hypothesis density filter along with hierarchical data associations (HDA) that isolate detection-to-track and track-to-track associations. Particularly, the cost matrix of various phases is solved using the Hungarian algorithm. For quick execution, detection information, such as bounding boxes and detection scores, is used in the HDA without visual feature information. Although this is an interesting work, the computational difficulty of the approach is not covered. In [30], a region proposal network (RPN) is developed that shares full-image convolutional features with the detection network. The RPN is a fully convolutional network that forecasts object bounds and scores at various positions.