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Image obtained by the whole set of Covariance Matrix columns related to character "2"
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Increased mobility and internationalization open new challenges to develop effective traffic monitoring and control systems. This is true for automatic license plate recognition architectures that, nowadays, must handle plates from different countries with different character sets and syntax. While much emphasis has been put on the license plate lo...
Citations
... Further, several studies have considered the recognition of multi-national plates. They have used multi-style processing by replacing the detection, segmentation and recognition pipelines [84], [85]. Table 4 provides a comparison of existing multi-style ALPR systems with the applied countries. ...
With the explosive growth in the number of vehicles in use, automated license plate recognition (ALPR) systems are required for a wide range of tasks such as law enforcement, surveillance, and toll booth operations. The operational specifications of these systems are diverse due to the differences in the intended application. For instance, they may need to run on handheld devices or cloud servers, or operate in low light and adverse weather conditions. In order to meet these requirements, a variety of techniques have been developed for license plate recognition. Even though there has been a notable improvement in the current ALPR methods, there is a requirement to be filled in ALPR techniques for a complex environment. Thus, many approaches are sensitive to the changes in illumination and operate mostly in daylight. This study explores the methods and techniques used in ALPR in recent literature. We present a critical and constructive analysis of related studies in the field of ALPR and identify the open challenge faced by researchers and developers. Further, we provide future research directions and recommendations to optimize the current solutions to work under extreme conditions.
... The efficiency of the proposed approach is improved and average rate of accuracy of the one-row LP is 96.93%, two-row LP is 95.82%. Mecocci et al. (2006) proposed generative models for license plate recognition by using a limited number of training samples. Increased mobility and internationalization are the challenges to develop effective traffic monitoring and control systems. ...
... In this paper, we address this problem with a cognitive approach. Contrary to recent works [19,30,40], we do not try to design the low-level steps to adapt to various LP types. We rather tackle the multi-national LPR by using cognitive loops, so that the high level analysis can automatically detect low-level step failures, and eventually adapt their parameters to compensate for them. ...
... Thus, country-specific approaches can artificially reach excellent performances, but they will fail at recognizing an even slightly different LP format. Although there is a huge literature in the general LPR topic, approaches relaxing LP format prior are relatively recent, and the amount of work is relatively limited [3,19,26,30,35,40]. In this paper, we denote these approaches as multi-national LPR systems. ...
License Plate Recognition (LPR) is mainly regarded as a solved problem. However, robust solutions able to face real-world
scenarios still need to be proposed. Country-specific systems are mostly, designed, which can (artificially) reach high-level
recognition rates. This option, however, strictly limits their applicability. In this paper, we propose an approach that can
deal with various national plates. There are three main areas of novelty. First, the Optical Character Recognition (OCR) is
managed by a hybrid strategy, combining statistical and structural algorithms. Secondly, an efficient probabilistic edit distance
is proposed for providing an explicit video-based LPR. Last but not least, cognitive loops are introduced at critical stages
of the algorithm. These feedback steps take advantage of the context modeling to increase the overall system performances,
and overcome the inextricable parameter settings of the low-level processing. The system performances have been tested in
more than 1200 static images with difficult illumination conditions and complex backgrounds, as well as in six different videos
containing 525 moving vehicles. The evaluations prove our system to be very competitive among the non-country specific approaches.
... What is more, the change of LP styles requires the method to adjust by itself so that the segmented and recognized character candidates can match best with an LP format. Several methods have been proposed for multi-national LPs or multi-format LPs in the past years[11,12]while few of them comprehensively address the style adaptation problem in terms of the above-mentioned factors. Some of them only claim the ability of processing multi-national LPs by re-defining the detection and segmentation rules or recognition models. ...
... What is more, the recognition method is not a learning-based method, which will limit its extensibility. In Ref.[12], Mecocci et al. propose a generative recognition method. Generative models (GM) are proposed to produce many synthetic characters whose statistical variability is equivalent (for each class) to that showed by real samples. ...
... Generative models (GM) are proposed to produce many synthetic characters whose statistical variability is equivalent (for each class) to that showed by real samples. Thus a suitable statistical description of a large set of characters can be obtained by using only a limited set of images[12]. As a result, the extension ability of character recognition is improved. ...
Despite the success of license plate recognition (LPR) methods in the past decades, few of them can process multi-style license plates (LPs), especially LPs from different nations, effectively. In this paper, we propose a new method for multi-style LP recognition by representing the styles with quantitative parameters, i.e., plate rotation angle, plate line number, character type and format. In the recognition procedure these four parameters are managed by relevant algorithms, i.e., plate rotation, plate line segmentation, character recognition and format matching algorithm, respectively. To recognize special style LPs, users can configure the method by defining corresponding parameter values, which will be processed by the relevant algorithms. In addition, the probabilities of the occurrence of every LP style are calculated based on the previous LPR results, which will result in a faster and more precise recognition. Various LP images were used to test the proposed method and the results proved its effectiveness.
... As already described in this survey, many algorithms utilize fixed plate geometry, color, and character fonts for LP location, segmentation, and character recognition. Little early research [39], [153], and [154] addressed this issue, still with several restrictions. ...
License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment.
For the efficient management of toll booths, automatic number plate recognition (ANPR) is a critical technology. Because of the growing number of vehicles on the road, there is a need for a more sophisticated and efficient system to handle the flow of traffic. The aim of this paper is to propose a solution to the problem of toll booth congestion in India using YOLOv7 with optical character recognition (OCR). The proposed system is aimed at recognizing Indian number plates in real-time and providing an end-to-end solution for the users in the form of a mobile application. The goal is to streamline the toll booth process and reduce the waiting time for commuters. This paper presents a comprehensive overview of the proposed solution and its performance evaluation.KeywordsANPRTollbooth managementYOLOv7Ensembled learningOCRIndian number plates
The ability to recognize and extract license plate information from still images or videos is an essential component of many modern transportation and public safety solutions. Although human review of some imagery is still employed for this purpose, much of this has given way to automated license plate recognition (ALPR). In fact, ALPR has in many ways transformed the public safety and transportation industries—helping enable modern tolled roadway solutions, providing tremendous operational cost savings via automation, and even enabling completely new capabilities in the marketplace (e.g., police cruiser-mounted license plate reading units). This chapter provides an overview of the technology behind ALPR solutions. The key modules typically found within an ALPR system are outlined, along with highlights of some of the most common methods employed to achieve state-of-the-art performance.
Automated license plate recognition (ALPR) is essential in several roadway imaging applications. For ALPR systems deployed in the United States, variation between jurisdictions on character width, spacing, and the existence of noise sources (e.g., heavy shadows, non-uniform illumination, various optical geometries, poor contrast, and so on) present in LP images makes it challenging for the recognition accuracy and scalability of ALPR systems. Font and plate-layout variation across jurisdictions further adds to the difficulty of proper character segmentation and increases the level of manual annotation required for training classifiers for each state, which can result in excessive operational overhead and cost. In this paper, we propose a new ALPR workflow that includes novel methods for segmentation- and annotation-free ALPR, as well as improved plate localization and automation for failure identification. Our proposed workflow begins with localizing the LP region in the captured image using a two-stage approach that first extracts a set of candidate regions using a weak sparse network of winnows classifier and then filters them using a strong convolutional neural network (CNN) classifier in the second stage. Images that fail a primary confidence test for plate localization are further classified to identify localization failures, such as LP not present, LP too bright, LP too dark, or no vehicle found. In the localized plate region, we perform segmentation and optical character recognition (OCR) jointly by using a probabilistic inference method based on hidden Markov models (HMMs) where the most likely code sequence is determined by applying the Viterbi algorithm. In order to reduce manual annotation required for training classifiers for OCR, we propose the use of either artificially generated synthetic LP images or character samples acquired by trained ALPR systems already operating in other sites. The performance gap due to differences between training and target domain distributions is minimized using an unsupervised domain adaptation. We evaluated the performance of our proposed methods on LP images captured in several US jurisdictions under realistic conditions.
Automated license plate recognition (ALPR) is a key capability in transportation imaging applications including tolling, enforcement, and parking, among others. An important module in ALPR systems is image classification that includes training classifiers for character recognition, commonly employed after detecting a license plate in a license plate image and segmenting out the characters from the localized plate region. A classifier is trained for each character in a one-vs-all fashion using segmented character samples collected from the actual camera capture site, where the collected samples are manually labeled by an operator. The substantial time and effort required for manual annotation of training images can result in excessive operational cost and overhead. In this paper, we propose a new method to minimize manual annotation required for training classifiers in an ALPR system. Instead of collecting training images from the actual camera capture site, our method utilizes either artificially generated synthetic license plate images or character samples acquired by trained ALPR systems already operating in other sites. The performance gap due to differences between training and target domain distributions is minimized using an unsupervised domain adaptation. The efficiency of the proposed method is shown on artificially generated and actual character samples collected from CA and NY.
