# Towards Automatic License Plate Recognition in Challenging Conditions
## 1 Introduction  
With the growth of big data, object detection and recognition have attracted excellent interest in research communities. This is because it can be used for a wide range of real-world applications, such as medical imaging, augmented reality, sports applications, independent driving, and video surveillance [1, 2, 3, 4, 5, 6]. Particularly, the license plate recognition (LPR) is getting more attention due to its widespread applications in various fields, for instance, traffic monitoring, toll collection, and criminal searches [7, 8]. Although many of the LPR systems, for instance [9, 10], are available in the literature, most of them have been validated and tested on a pre-defined LP specification. Few of these works are also capable of processing multiple LPs. The LPR systems can be categorized into two major categories, which are (i) traditional LPR and (ii) deep learning-based LPR systems. Traditional methods process limited features and utilize hand-crafted features, for example, contours, colors, and edges, to locate the LP. Deep learning-based techniques automatically learn robust features from the data and have recently produced promising results. Deep learning-based techniques consider LP detection as object detection and analyze the recognition as an optical character recognition (OCR) process. Since the number of characters processed by any LPR method is limited, character recognition is also considered an object detection process, so that LP detection and recognition are handled simultaneously. To develop and analyze a LPR algorithm that can deal with multi-style license plate recognition, there are several challenges, as briefly described below. **Lack of Standard LPs**: Standardizing the LPs is a significant challenge throughout the world. For instance, license plates in Macao, China, have to meet a strict standard, as shown in Figure 1(a) [11], whereas the LP of Hong Kong usually has 1-8 characters and Macao LPs are composed of five to six characters, as shown in Figure 1b,c, respectively. General observation in Figure 1a indicates that the first column has an entirely different appearance than the second column. Similarly, the second and third columns have huge contrast and appearance variations. Few of the plates have yellowish and greenish backgrounds, while few have colorless backgrounds. Moreover, the distance between the characters on the plates shown in the first column takes up much less space than those in the third column. **Lack of LP Outlines**: Many times, the license plates have no outlines, which makes it an extremely difficult task to classify. This task becomes more challenging when the color of the vehicle is the same as that of the LPs. One such example is indicated in Figure 1b. The top image shown in Figure 1b contains a black background and white ground on a black vehicle. Moreover, the bottom vehicle shown in Figure 1b shows a LP case that has a white background due to the white color of the car and a black front ground. These two cases may appear trivial, but for any machine learning algorithm, the aforedescribed task is not easy. The algorithm should be capable enough to distinguish such cases accurately. **The appearance of LPs**: Another challenge in LP localization and recognition is to accurately handle the appearance and occlusion challenges of license plates installed at various locations on a vehicle. If a vehicle has more than one license plate, then characters get matched with the background of other plates, which makes it difficult to distinguish LP character regions. A few cases of this challenge are shown in Figure 1c. Figure 1c depicts two license plates with total complement variations on the top vehicle. Moreover, along with the low-intensity light beam, one plate has a white background with black characters on the plate region, while the other plate has a black background with white alphabets on top. In both cases, the vehicle color appears blackish-green. In each of these cases, it poses a significant challenge to any recognition algorithm.