# Towards Automatic License Plate Detection
###### Abstract  
Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles' license plates in images is a critical step that has a substantial impact on any ALPD system's recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications.  
license plate detection; estimation; segmentation; object tracking; vehicle detection 20 operations to locate the plate. Our proposed LPD method is motivated by common facts, such as license plates commonly have high edge density. In addition, characters on license plates are generally displayed in a horizontal orientation with nearly identical height. The major contributions of this study are as follows:  
* We investigate challenging factors, such as very low or high luminance, bad lighting conditions, extreme reflective glare, and low-resolution on a challenging PKU dataset. In addition, we also introduce a novel mechanism that we refer as License Plate Localization Module (LPLM) in this manuscript. The proposed LPLM is efficient enough to handle challenging scenarios, such as extreme reflective glare, low, and high luminance variations. The LPLM works in near real-time and yields much higher license plate detection accuracy than few of the methods compared therein. To the best of our knowledge, illumination rectification has never been used in recently published works.
* We integrate the Faster R-CNN with our own developed License Plate Localization Module (LPLM), which emerges as a unique method to accurately detect different license plates that are provided by the PKU dataset developers. Moreover, the proposed method is significantly easier to use than previously reported methods, as it produces promising outcomes in difficult situations.
* Our proposed license plate detection algorithm outclasses several recent methods in terms of detection accuracy, precision, recall, and computational efficiency. Moreover, the proposed method is user-friendly and takes less than 3 s to detect the license plate in 1600 \(\times\) 1200 pixels input image.  
This paper is structured as follows. Section 2 briefly describes the recent developments in the license plates detection domain. The proposed method is elaborated in Section 3. Detailed simulation results are discussed in Section 4 along with the discussions and findings. Finally, Section 5, concludes the paper and hints towards the possible future directions. For the easy understandings of the readers, Table 1 lists the commonly used acronyms, symbols, and notations that are used extensively in this paper along with their meanings.