## 1 Introduction  
Automatic license plate recognition (ALPR) is a significant topic in the field of intelligent transport systems (ITS) and remains ever challenging in the research era of image processing and computer vision. This is the framework that is used widely to extract the license plate registration number from digital images for vehicle identification. This system also does not require any additional hardware like transmitter or responder to detect vehicles as every vehicle is identified by its license plate. Numerous potential applications have urged researchers to pay more attention to developing an efficient and sophisticated ALPR system. The law enforcement agencies are widely using this system for traffic monitoring, congestion control, borders or restricted area security and also to detect suspicious or theft vehicles. LPR system is also being used in smart parking areas and smart toll stations for flexible entry and exit of vehicle, smooth traffic flow, deduct fee or fine and to enhance the security. Moreover, it can be installed in intelligent vehicles to recognize the identity of neighboring vehicles for communication purposes. License plate detection (LPD) and license plate recognition (LPR) are two major modules of an ALPR system. LPR module can be further split into license plate character segmentation (LPCS) and license plate character recognition (LPCR). In recent years, particularly focused work has been seen for multinational ALPR systems, but it has been done only for license plate detection and verification [1; 2; 3; 4; 5]. Every module has its own importance but LPR is comparatively more important as most required information is extracted at this stage and this is a difficult task, specifically in the case of multi-style LPs for multinational vehicles. The whole system will be useless if we could not get a whole registration number at the LPR stage. In order to complete the system, the proposed work has been done for license plate recognition part that further splits into LPCS and LPCR stages. Most of the work is seen only for specific types of LPs in existing literature and their effectiveness is limited as it cannot be applied to another region. Moreover, change in license plate regulations would lead to the failure of such specific methods. Various countries do not have standardized LPs and also allow customized license plates that add further complications. In addition, many countries have a lot of diversity in their license plates. Therefore, there is a need to develop a generalized framework for the recognition of multinational vehicle license plates to cater above-mentioned issues. Generally, challenges that are involved in effective character segmentation and recognition of license plates are illumination variance, rotation, shadow and border touched characters due to screws or noise, but some more challenges are involved when we deal with multinational vehicle license plates. They have different background and foreground colors, complex backgrounds containing different shapes and writings, and different shape and size of LPs and characters as shown in Figure 1.  
A unique part-based approach is developed that gives very promising results for character segmentation of multinational vehicle license plates despite the above-mentioned challenges. The RGB color-based foreground polarity detection model and character height estimation filter are proposed to accomplish the license plate character segmentation task effectively. To deal with multi-style backgrounds, thresholding under various illumination conditions, skew correction and border touched characters for multinational VLPs are salient features of proposed segmentation method. For the recognition of segmented characters, multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used as classifier to produce target labels.