In recent years many CNN-based approaches are introduced to accomplish segmentation free LPR task as discussed in. Sliding window technique has also been used for America and Iran, but this method requires prior knowledge of window size and it is computationally complex. Maximally stable extremal regions (MSER) is also very famous method for LPCS as used in for Chinese license plates. The first image is converted into binary then horizontal and vertical projection is used to separate the characters. Usually, this technique is used with one of the binarization methods (OTSU, Adaptive, MET, etc.). The projection-based methods have also been used for China and Iran respectively for LPCS. This method is suitable only for highly contrasting images and very sensitive to shadow. CIE-LAB color space, OTSU segmentation and CCA are used to extract characters from a Pakistani vehicle license plate. The CCA and Contour detection is used for character segmentation in Bangladesh and Iraq license plates. The CCA and kohonen neural network is used to segment characters from Indian LPs. The two-stage character segmentation approach is proposed for Chinese LPs by using CCA and bank of harrow-shaped filter (HSF). The research conducted in claims to be implemented for multinational vehicle LPR but was only tested on Israeli and Bulgarian LPs. 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. 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. Therefore, there is a need to develop a generalized framework for the recognition of multinational vehicle license plates to cater above-mentioned issues. In addition, many countries have a lot of diversity in their license plates. Various countries do not have standardized LPs and also allow customized license plates that add further complications. Moreover, change in license plate regulations would lead to the failure of such specific methods. 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, it can be installed in intelligent vehicles to recognize the identity of neighboring vehicles for communication purposes. 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. 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. Numerous potential applications have urged researchers to pay more attention to developing an efficient and sophisticated ALPR system. 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. This is the framework that is used widely to extract the license plate registration number from digital images for vehicle identification. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions.Īutomatic 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. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments.
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