License Plate Region Research Articles

Vehicle Number Plate Recognition Using Raspberry Pi

Vehicle number plate recognition (VNPR) systems have gained significant attention due to their wide range of applications in traffic management, parking enforcement, and security systems. This abstract presents an overview of a VNPR system that utilizes computer vision techniques and machine learning algorithms for license plate detection and recognition. The system is implemented using a Raspberry Pi platform, which provides a cost-effective and portable solution. The system follows a systematic approach to perform license plate recognition. Firstly, the camera captures images or video frames, which are then processed using the OpenCV library to enhance the visibility and quality of the license plates. License plate detection algorithms, employing edge detection or machine learning-based object detection techniques, are applied to locate and extract the license plate regions in the processed images. Following license plate detection, character segmentation techniques are utilized to isolate individual characters on the plate. Finally, integrated into the system, the Tesseract OCR engine recognizes the segmented characters and extracts the text information, effectively providing the license plate data.

An efficient automated vehicle license plate recognition system under image processing

<span lang="EN-US">An automated vehicle license plate recognition system using image processing techniques identifies vehicle numbers without human interference. This system has significant impact because of its good application in various fields like car parking, access control, speed control, automatic toll collection, border security, traffic violence detection and surveillance applications. This paper presents a methodology that is quite simple but at the same time very much efficient and this system consists of four sequential modules which are preprocessing, number plate extraction, number plate character segmentation and character recognition. Preprocessing aims to improve the image quality that is captured in various illumination conditions and stick out outstanding information that we need, which is favorable to subsequent processing including extraction, segmentation and recognition. After preprocessing various morphological operations are applied to extract the desired license plate region. Then for segmentation the bounding box method is applied that segments each letter and number present on the license plate region. Finally, template matching is applied in identifying all segmented characters present in the license plate image. The experimental results showed that the proposed system can recognize license plate characters efficiently with higher accuracy. Using MATLAB software, the proposed method attains recognition accuracy of 94.17%.</span>

ResNet and LSTM Based Accurate Approach for License Plate Detection and Recognition

The identification and recognition of automatic license plates (ALP) are critical for traffic surveillance, parking management, and preserving the rhythm of modern urban life. In this paper, a deep learning-based method is proposed for ALP. In the proposed work, the license plate region is initially segmented in a given vehicle image, and the plate number and city region are extracted from the segmented license plate region. Residual neural networks (ResNet) architecture-based deep feature extraction is considered. The fully connected layer of the ResNet model is used to obtain the deep features for the cropped Arabic numbers and city regions, respectively. The extracted features are fed into the sequential input layer of the Long Short-Term Memory (LSTM) classifier. Various experiments are carried out on a dataset that was collected in the northern Iraq region and the classification accuracy score is used for performance evaluation. According to the obtained results, the proposed method is effective where the calculated accuracy scores were 98.51% and 100% for Arabic numbers and city regions, respectively. The performance comparison of the proposed method with some of the existing methods indicates the high performance of the proposed study.

Comparison of Vehicle License Plate Detection Algorithms and LP Character Segmentation and Recognition using Image Processing

In the last couple of decades, the number of vehicles has increased drastically, consequently, it is becoming difficult to keep track of each vehicle for purpose of law enforcement and traffic management. License Plate Detection is used increasingly nowadays for the same. The system performing the task of License Plate detection is known as the LPR system which generally consists of three steps: Detection of the License plate, Segmentation of License plate characters, and Recognition of the characters of the License Plate (LP). But in real-world scenarios, the various lighting conditions, camera angle, and rotation degrades the accuracy of License Plate region detection, which in turn causes inaccurate segmentation and recognition of the license plate characters hence leading to low accuracy of the LPR systems. Therefore, it is vital to consider the most promising algorithm or technique for LP detection. In this paper, we will be analyzing and comparing five different methods for license plate detection: Morphological reconstruction, Sobel Operator, Top Hat Transform, Histogram processing, and Canny Edge detection. We will be experimentally applying these techniques on real-time captured vehicle images, using the Bounding Box algorithm for character segmentation, performing license plate character recognition using Template matching, and subsequentially evaluating and demonstrating the LPR system that promises the most accurate and efficient results.

Automatic Number Plate Recognition Using Image Processing

Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their registration number issued by government. ANPR is often used in the detection of stolen vehicles, traffic surveillance system. Our project presents a model in which the vehicle license plate image is obtained by the digital cameras and the image is processed to get the number plate information. A vehicle image is captured and processed using various methods. Vehicle number plate region is extracted using the deep neural networks. Optical character recognition is implemented using certain machine learning algorithms for the character recognition. The system is implemented using deep neural network model, machine learning algorithms and is simulated in python, and its performance is tested on real images. It is observed that the developed model successfully detects the license plate region and recognizes the individual characters. There are various recognition strategies that have been produced and number plate recognition systems are today used in different movement and security applications, such as access and border control, parking, or tracking of stolen vehicles.

Traffic Violation Detection System Using Image Processing

Over the last three decades, the global population of human beings has increased at an exponential rate, resulting in an equal rise in the number of vehicles owned and used globally. Vehicle traffic is a major economic component in both urban and rural areas, and it requires proper management and monitoring to ensure that this mass of vehicles coexists as smoothly as possible. The amount of vehicular traffic on roads around the world, with Nigeria as a case study, results in varying degrees of traffic rule violations, especially red light jumping. To arrest offenders and resolve the weaknesses and failures of human traffic operators who cannot be everywhere at once, efficient traffic violation and number plate recognition systems are needed. There are several methods for reading characters, which can be alphabets, numbers, or alphanumeric. To minimize processing time and computational load on the machine, this research proposed k-Nearest Neighbour for plate number character recognition. The system was developed and evaluated. From the result, the localization of license plate regions within an image was 92 percent accurate, and character recognition was 73 percent accurate.

A Flexible Approach for Automatic License Plate Recognition in Unconstrained Scenarios

Automatic License Plate Recognition is a crucial task for several applications related to Intelligent Transportation Systems, from access control to traffic monitoring. Most existing approaches are focused on a specific setup (e.g., toll control) or a single license plate (LP) region (e.g., European, US, Brazilian, Taiwanese, etc.), which limits their application. This work proposes a complete ALPR system focusing on unconstrained capture scenarios, where the LP might be considerably distorted due to oblique views. We present an Improved Warped Planar Object Detection Network (IWPOD-NET) that is able to detect the four corners of an LP in a variety of conditions, so that it can be warped to a fronto-parallel view and alleviate perspective-related distortions. Given the rectified LP, we test two different Optical Character Recognition (OCR) methods based on object detection. Our experimental results show that the proposed detector is competitive with state-of-the-art (SOTA) methods using a very limited training set. Regarding the full ALPR results, our method achieves top-scoring results for several datasets that include a variety of capture conditions and vehicle types (in particular, motorcycles).

Vehicle license plate recognition for fog‐haze environments

The technique of vehicle license plate recognition can recognize and count the vehicles automatically, and thus many applications regarding the vehicles are greatly facilitated. However, the recognitions of vehicle license plates are extremely difficult especially in some fog-haze environments because the fog and haze blur the boundaries and characters of license plates significantly, which makes the license plates hard to be detected or recognised. To this end, this paper proposes a vehicle License Plate Recognition method for Fog-Haze environments (LPRFH). In LPRFH, a dark channel prior algorithm based on the local estimation of atmospheric light value is applied to dehaze the blurred images preliminarily. Then, the images are further dehazed, and the license plate regions are detected through a Joint Further-dehazing and Region-extracting Model on basis of an object detection convolution neural network. Finally, the image super-resolution is accomplished with a convolution-enhanced super-resolution convolutional neural network, and hence the characters of license plates can be recognised successfully. Extensive experiments have been conducted, and the results indicate that LPRFH can recognise the license plates accurately even in some severe fog-haze environments.

Super-resolution of License-plates Using Weighted Interpolation of Neighboring Pixels from Video Frames

Recognizing the license plate from a set of low-resolution video frames using an Optical Character Recognition system (OCR) is a very challenging task. OCR systems fail to properly work in this condition. The use of high-quality cameras is a costly solution to this situation. To overcome this problem, we propose a weighted interpolation method that enhances the resolution of the license plate, using consecutive frames of a video. For this purpose, first, we register the low-resolution video frames of the license plate to the reference license plate in two steps. In the first step, a coarse registration is performed by matching the SURF features. Then a fine registration on the license plate region is performed using the phase correlation technique. After registration, the reference image of the license plate is up-sampled to the desired scale. We propose a method for estimating the intensity of pixels in the up-sampled image with an unknown value. In this method, we use a weighted averaging strategy to estimate the intensity of unknown pixels using the neighboring pixels from video frames. The obtained super-resolution is suitable for OCR. Experimental results show that applying the proposed method on low-resolution frames of the license plate, improves the quality of the license plate significantly.

Real-time license plate detection and recognition using deep convolutional neural networks

Automatic License Plate Recognition (ALPR) is an important task with many applications in Intelligent Transportation and Surveillance systems. This work presents an end-to-end ALPR method based on a hierarchical Convolutional Neural Network (CNN). The core idea of the proposed method is to identify the vehicle and the license plate region using two passes on the same CNN, and then to recognize the characters using a second CNN. The recognition CNN massively explores the use of synthetic and augmented data to cope with limited training datasets, and our results show that the augmentation process significantly increases the recognition rate. In addition, we present a novel temporal coherence technique to better stabilize the OCR output in videos. Our method was tested with publicly available datasets containing Brazilian and European license plates, achieving accuracy rates better than competitive academic methods and a commercial system.

Cascaded deep learning-based efficient approach for license plate detection and recognition

Automatic license plate (ALP) detection and recognition is an important task for both traffic surveillance and parking management systems, as well as being crucial to maintaining the flow of modern civic life. Various ALP detection and recognition methods have been proposed to date. These methods generally use various image processing and machine learning techniques. In this paper, a cascaded deep learning approach is proposed in order to construct an efficient ALP detection and recognition system for the vehicles of northern Iraq. The license plates in northern Iraq contain three regions, namely a plate number, a city region, and a country region. The proposed method initially employs several preprocessing techniques such as Gaussian filtering and adaptive image contrast enhancement to make the input images more suited to further processing. Then, a deep semantic segmentation network is used in order to determine the three license plate regions of the input image. Segmentation is then carried out via deep encoder-decoder network architecture. The determined license plate regions are fed into two separate convolutional neural network (CNN) models for both Arabic number recognition and the city determination. For Arabic number recognition, an end-to-end CNN model was constructed and trained, whilst for the city recognition, a pretrained CNN model was further fine-tuned. A new license plate dataset was also constructed and used in the experimental works of the study. The performance of the proposed method was evaluated both in terms of detection and recognition. For detection, recall, precision and F-measure scores were used, and for recognition, classification accuracy was used. The obtained results showed the proposed method to be efficient in both license plate detection and recognition. The calculated recall, precision and F-measure scores were 92.10%, 94.43%, and 91.01%, respectively. Moreover, the classification accuracies for Arabic numbers and city labels were shown to be 99.37% and 92.26%, respectively.

License plate detection for multi-national vehicles – a generalized approach

License plate detection for vehicle identification is one of the key problems for traffic surveillance in urban areas. Most of the existing methods that can handle multiple license plates are country-specific as color information has been typically targeted. In this paper, we propose a real-time multiple license plate detection method feasible for multi-national vehicles having variable colors, sizes and geometrical attributes. A region-of-interest is initially identified for each vehicle using a fuzzy inference system based on the salient feature of its rear lights as license plates generally exist in a vicinity of these lights. Due to the abundance of edges within the license plate region, a local recursive analysis approach is utilized to locate the license plate candidate within each region-of-interest after tilt correction using a rear-light alignment technique. To verify the detected region as a true license plate, a unique combination of local image features is used to achieve high precision. The proposed method has been tested on 2200 images taken during various weather and illumination conditions to detect 5379 license plates out of 5945 available vehicles with 90.5% accuracy. The proposed approach outperforms the conventional and deep learning methods to achieve superior performance with the ability of being applied to multi-national vehicles.

Extraction of Character Regions through Machine Learning and Filtering

Characters in images are able to provide main information of the image. Therefore, it is important to analyze various kinds of image data and accurately extract the characters in images. This study proposes a new method of excluding background regions and accurately detecting character regions from input images with the uses of MCT features and Adaboost algorithm. The proposed method first extracts candidate character regions from input images with the uses of MCT features and Adaboost algorithm. It then excludes non-character regions and detects real character regions from the extracted candidate regions with the use of geometrical features. In the experiment of this study, the proposed method more robustly detected character regions from various input color images than a conventional method. For performance comparison, this study compared the method based on existing texture analysis and the proposed method. In this study, to qualitatively evaluate the performance of the proposed method of extracting license plate regions, the accuracy measure was defined. The measure is used to show the ratio of the accurately extracted character regions to all character regions of an image. The conventional method using the frequency factor-based texture information had many errors of character region detection, since it failed to execute binarization of background and character regions properly. On contrary, the proposed method made use of MCT features and Adaboost algorithm, effectively filtered candidate regions with the use of geometrical features, so that it detected character regions more accurately. The proposed character detection method is expected to be usefully applied to the fields of pattern recognition and image processing, such as store sign recognition and license plate recognition.

License Plate Detection and Facial Analysis Using TensorFlow Deep Learning Algorithm

Nowadays license plate recognition has been applied in car access control, toll collection and other applications. Even though they exist, car thefts and illegal use of other proprietor’s license plate remain a problem. To deal with this, a computational programmed controlled framework is being developed. Also, facial analysis algorithm is implemented so as to create awareness among the common public. The way forward is to use an improved technology combination of License Plate Detection and Facial Analysis using artificial intelligence, in which vehicle image is captured by high resolution CCD camera and the license plate region is determined by image processing algorithms and facial analysis is done by using FaceNet and TensorFlow. The characters of the license plate is separated by segmentation and processed using the Canny Edge and Blob Coloring algorithm and the facial analysis is done using Facenet of TensorFlow.

Automatic Vehicle License Plate Extraction Using Region-Based Convolutional Neural Networks and Morphological Operations

The number and range of the candidate vehicle license plate (VLP) region affects the result of the VLP extraction symmetrically. Therefore, in order to improve the VLP extraction rate, many candidate VLP regions are selected. However, there is a problem that the processing time increases symmetrically. In this paper, we propose a method that allows detecting a vehicle license plate in the real-time mode. To do this, the proposed method makes use of the region-based convolutional neural network (R-CNN) method and morphological operations. The R-CNN method is a deep learning method that selects a large number of candidate regions from an input image and compares them to determine whether objects of interest are included. However, this method has limitations when used in real-time processing. Therefore, to address this limitation in the proposed method, while selecting a candidate vehicle region, the selection range is reduced based on the size and position of the vehicle in the input image; hence, processing can be performed quickly. A vehicle license plate is detected by performing a morphological operation based on the edge pixel distribution of the detected vehicle region. Experimental results show that the detection rate of vehicles is approximately 92% in real road environments, and the detection rate of vehicle license plates is approximately 83%.

An Efficient Method for License Plate Detection and Recognition using OCR

This paper presents an efficient method for license plate detection and recognition that will aid in the development of an intelligent transport system. To significantly accelerate license plate localization a simple image pre-processing method is first used without reducing detection performance compared with that obtained using original image. Line density filter approach is used to extract the license plate regions, there by significantly reducing area to be analysed for license plate localization. In license plate recognition, the characters are recognized from these detected license plates by using optical character recognition and the owner details are displayed. The aim of this paper is to serve the purpose of accurately localizing vehicle license plates from complex scenes in real time that would help motor vehicle department in various ways.

Deep‐learning‐based license plate detection method using vehicle region extraction

A new license plate detection method for challenging environments is proposed. Background clutters are common in road scene images and the detection of license plates (occupying only a small part of an image) is considered as a difficult problem. In order to address this problem, a two-step approach is developed: first vehicle regions are detected and the license plate in each vehicle region is localised. This vehicle region detection based approach provides scale information and limits search ranges in license plate detection, so that one can reliably detect license plate regions. To be precise, the faster region-based convolutional neural network algorithm for the vehicle region detection is adopted and candidates for license plates in each detected region with the hierarchical sampling method are generated. Finally, non-plate candidates are filtered out by training a deep convolutional neural network. The proposed method is evaluated on the Caltech dataset and the method showed a precision of 98.39% and a recall of 96.83%, which outperforms conventional methods.

Iraqi Kurdistan Vehicle License Plate Recognition System based on Client-server Network

Vehicle license plate recognition (VLPR) system has a wide range of applications in real life, such as parking lots, private and public entrances, theft and border control. This paper presents a system overview of client-server license plate recognition system for vehicles in Kurdistan Region of Iraq besides, the vehicle type and city in each plate is identified. The client/server model consist of two components called client-side and server-side. The images are provided by the client side and will be sent over the server to be processed. The process of license plate recognition is done in the server side with the integration with the Matlab software. The proposed recognition system is developed based on digital images and the main component of the system consist of: preprocessing, license plate detection, segmentation and recognition process. A preprocessing step using image enhancement, filtering, and adaptive thresholding is employed. For localizing and extracting the license plate region, Haar wavelet transformation and edge detection algorithm are used. license plate number segmentation is carried out using bounding box based morphological operations and finally color histogram technique and correlation coefficient based similarity measurement are used for recognition task. The experimental results show that our proposed system for the license plate detection and recognition process have obviously higher recognition accuracy rate.

A Vehicle License Plate Recognition System Using Morphological ROI (Region of Interest) Map Generated from Morphology Operation

In this paper, morphology features are used to improve license plate region and Morphological ROI (MROI) map that consists of a standard deviation map of open and close images is proposed. Unlike conventional morphology techniques, MROI map is used to improve the detection of license plate in a complex background. Furthermore, distribution of the MROI map was analyzed to determine a threshold level, generate binary code images, and finally, determine the vehicle license plate region by using the features of the plate such as aspect ratio and vertical edge intensity. The proposed method could improve the error rate by approximately 4-7% depending on the pattern of the images.

로드뷰 영상에서 번호판 영역의 저해상도 특징을 이용한 원거리 자동차 번호판 영역 검출

본 논문은 포털 사이트에서 서비스 되고 있는 로드뷰 영상에서 개인정보 보호를 위해 자동차 번호판 영역을 검출하는 방법을 제안한다. 로드뷰 영상에서 번호판 영역은 거리에 따라 서로 다른 특징을 갖고 있으며, 특히 원거리의 번호판 영역은 저해상도 특징으로 인해 번호판 영역을 검출하는데 어려움이 있다. 따라서 본 연구에서는 근거리에 있는 번호판 영역은 에지 특징을 이용하고 원거리에 있는 번호판 영역은 MSER 특징을 이용하여 번호판 영역을 검출하는 기법을 제안하였다. 각각의 방법으로 검출된 영역을 번호판 후보 영역으로 선정하고, 자동차 번호판의 숫자는 구조적 특징을 갖기 때문에 이를 이용하여 최종적인 번호판 영역을 검출하였다. 실험결과, 다양한 로드뷰 영상에서 precision 75%, recall 93%, 그리고 F-Score 80%의 성능평가 결과를 얻었다. For privacy protection, we propose a vehicle license plate region detection method in road view image served from portal site. Because vehicle license plate regions in road view images have different feature depending on distance, long distance vehicle license plate regions are not detected by feature of low resolution. Therefore, we suggest a method to detect short distance vehicle license plate regions by edge feature and long distance vehicle license plate regions using MSER feature. And then, we select candidate region of vehicle license plate region from detected region of each method, because the number of the vehicle license plate has a structural feature, we used it to detect the final vehicle license plate region. As the experiment result, we got a recall rate of 93%, precision rate of 75%, and F-Score rate of 80% in various road view images.