License Plate Recognition Algorithm Research Articles

A License Plate Recognition Method Based on Wavelet Transform and Symmetrical Principal Component Analysis Algorithm

In the past, the license plate recognition algorithm has some shortcomings, such as low recognition rate, slow speed of recognition, inaccurate license plate positioning. This paper proposes a new license plate location algorithm based on wavelet transform and the principal component analysis algorithm is used to feature extraction.The experimental results show that this method can reduce the amount of computation and improve the system recognition rate.

Повышение достоверности результатов поиска автомобильных номеров с использованием модифицированного алгоритма распознавания государственных регистрационных знаков

Повышение достоверности результатов поиска автомобильных номеров с использованием модифицированного алгоритма распознавания государственных регистрационных знаков

License Plate Recognition Algorithm for Passenger Cars in Chinese Residential Areas

This paper presents a solution for the license plate recognition problem in residential community administrations in China. License plate images are pre-processed through gradation, middle value filters and edge detection. In the license plate localization module the number of edge points, the length of license plate area and the number of each line of edge points are used for localization. In the recognition module, the paper applies a statistical character method combined with a structure character method to obtain the characters. In addition, more models and template library for the characters which have less difference between each other are built. A character classifier is designed and a fuzzy recognition method is proposed based on the fuzzy decision-making method. Experiments show that the recognition accuracy rate is up to 92%.

에지기반 세그먼트 영상 생성에 의한 차량 번호판 인식 시스템

스마트시티 프로젝트의 일환으로 실시간 차량 번호판 인식에 관한 연구들이 활발하게 진행되고 있다. 도로상에 설치된 CCTV에서 트리거 신호 없이 주행하는 차량 영상을 획득할 경우에는 번호판의 기하학적 왜곡이나 화질의 저하가 발생하여 번호판 인식이 어려워 질 수 있다. 본 논문에서는 트리거 신호를 이용하지 않은 상태에서 입력되어 기하학적 왜곡이나 화질의 저하가 발생된 차량 영상에도 강한 에지기반 문자 세그먼트 영상생성 기법의 차량 번호판 인식시스템을 제안하였다. 제안한 실시간 차량 번호판 인식 알고리즘을 도로상에 설치된 CCTV에 구현하고 일주일 동안 번호판 인식 실험을 수행해 본 결과 1일 평균 1,535 대의 통과 차량에 대해서 97.5%의 번호판 검출률을 얻을 수 있었으며 검출된 번호판에 기록된 문자들의 99.3%를 인식할 수 있었다. The research of vehicle license plate recognition has been widely studied for the smart city project. The license plate recognition can be hard due to the geometric distortion and the image quality degradation in case of capturing the driving car image at CCTV without trigger signal on the road. In this paper, the high performance vehicle license plate recognition system using edge-based segment image is introduced which is robust in the geometric distortion and the image quality degradation according to non-trigger signal. The experimental results of the proposed real time license plate recognition algorithm which is implemented at the CCTV on the road show that the plate detection rate was 97.5% and the overall character recognition rate of the detected plates was 99.3% in a day average 1,535 vehicles for a week operation.

An Improved Bernsen Algorithm Approaches For License Plate Recognition

An Improved Bernsen Algorithm approaches for License Plate Recognition (LPR) applied to the intelligent transportation system. LPR is an image processing technology used to identify the vehicles by their license plates. License plate recognition is mainly based on two methods. One is License plate detection method which is based on Improved Bernsen Algorithm and the other is license plate character recognition method. The proposed LPR algorithm consists of three modules: 1) Locating the license plates; 2) Segmenting the characters; and 3) Identifying the license characters. The algorithm was tested with 100 images, such as an Indian vehicle images using different backgrounds. The experimental results show the algorithm can divide the character of plate effectively.

메쉬 및 세선화 기반 특징 벡터를 이용한 차량 번호판 인식

This paper proposes an effective algorithm of license plate recognition for industrial applications. By applying Canny edge detection on a vehicle image, it is possible to find a connected rectangular, which is a strong candidate for license plate. The color information of license plate separates plates into white and green. Then, OTSU binary image processing and foreground neighbor pixel propagation algorithm CLNF will be applied to each license plates to reduce noise except numbers and letters. Finally, through labeling, numbers and letters will be extracted from the license plate. Letter and number regions, separated from the plate, pass through mesh method and thinning process for extracting feature vectors by X-Y projection method. The extracted feature vectors are compared with the pre-learned weighting values by backpropagation neural network to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.

An Algorithm for License Plate Recognition Applied to Intelligent Transportation System

An algorithm for license plate recognition (LPR) applied to the intelligent transportation system is proposed on the basis of a novel shadow removal technique and character recognition algorithms. This paper has two major contributions. One contribution is a new binary method, i.e., the shadow removal method, which is based on the improved Bernsen algorithm combined with the Gaussian filter. Our second contribution is a character recognition algorithm known as support vector machine (SVM) integration. In SVM integration, character features are extracted from the elastic mesh, and the entire address character string is taken as the object of study, as opposed to a single character. This paper also presents improved techniques for image tilt correction and image gray enhancement. Our algorithm is robust to the variance of illumination, view angle, position, size, and color of the license plates when working in a complex environment. The algorithm was tested with 9026 images, such as natural-scene vehicle images using different backgrounds and ambient illumination particularly for low-resolution images. The license plates were properly located and segmented as 97.16% and 98.34%, respectively. The optical character recognition system is the SVM integration with different character features, whose performance for numerals, Kana, and address recognition reached 99.5%, 98.6%, and 97.8%, respectively. Combining the preceding tests, the overall performance of success for the license plate achieves 93.54% when the system is used for LPR in various complex conditions.

왜곡 불변 차량 번호판 검출 및 인식 알고리즘

Automatic vehicle license plate recognition technology is widely used in gate control and parking control of vehicles, and police enforcement of illegal vehicles. However inherent geometric information of the license plate can be transformed in the vehicle images due to the slant and the sunlight or lighting environment. In this paper, a distortion invariant vehicle license plate extraction and recognition algorithm is proposed. First, a binary image reserving clean character strokes can be achieved by using a DoG filter. A plate area can be extracted by using the location of consecutive digit numbers that reserves distortion invariant characteristic. License plate is recognized by using neural networks after geometric distortion correction and image enhancement. The simulation results of the proposed algorithm show that the accuracy is 98.4% and the average speed is 0.05 seconds in the recognition of 6,200 vehicle images that are obtained by using commercial LPR system.

휴대단말기 영상에서의 기하학적 정보를 이용한 차량 번호판 인식

현재 카메라로 입력된 문자 영상 처리를 위한 기술 개발이 국내외에서 활발히 이루어지고 있으나 낮은 정확도나 처리시간이 많이 걸리는 문제점 등으로 실용화 비율은 현저히 낮다. 본 논문에서는 휴대단말기 카메라에서 얻은 영상으로 기하학적 정보를 이용한 차량 번호판 인식 방법을 제안한다. 휴대폰 영상의 낮은 해상도와 부족한 명암대비, 각도 차이 등을 고려한 전처리 작업 수행 후 투영에지 누적 계산을 통해 추출된 번호판 영역에서 체인코드와 Thickness 정보를 이용하여 문자를 인식한다. 제안된 알고리즘은 기존의 차량 번호판 인식 알고리즘의 문제점과 휴대단말기 영상 처리라는 점 등을 고려하여 가볍고 처리 시간을 단축시켰으며, 실험 결과 95%의 문자 인식 성공률을 얻었다. 향후 연구로 원거리 영상이나 모션블러가 가미된 영상에서의 번호판 인식 알고리즘을 모색할 예정이다. Recently, the character image processing technology using portable device camera image at home and abroad are actively conducted, but Practical use are lower rate because of accuracy and time-consuming process problems. In this paper, we propose the license plate recognition method based on geometric information from portable device camera image. In the extracted license plate region we recognize characters using the chain code and the Thickness information through the cumulative projected edge after performing the pre-processing work considering the angle difference, the contrast enhancement and the low resolution from portable device camera image. The proposed algorithm is effective and accurate recognition by light and reducing the processing time. And, the results from the character recognition success rate was 95%. In the future, we will research about license plate recognition algorithm using long distance image or added motion blur image.

Tracking Large Trucks in Real Time with License Plate Recognition and Text-Mining Techniques

Large-truck speeds on Interstate highways are not only a safety concern but have also become a problem with respect to air quality and energy consumption. Many cities have reduced truck speed limits to curb emissions. As potentially key building blocks of a proposed automated large-truck speed monitoring and enforcement system, license plate recognition (LPR) and plate-matching algorithms are the focus of this study. Present LPR systems are not perfect and can fail to read one-fifth to one-half of the characters on license plates, depending on various factors. Fortunately, even when LPR fails to read all characters on license plates, it is still able to read most of the characters with an appreciable degree of accuracy. By employing a text-mining technique called edit distance (with judicious use of travel time), this study demonstrates that it is possible to match 97% of license plates when only just more than 60% of the same plates were read correctly by LPR units at two different locations. This high matching rate does not entail a high false-positive matching rate either (about 2%). This paper presents the plate-matching algorithm in detail and provides statistics resulting from a field study conducted on I-40 in 2007. Several promising research directions for better matching efficiency and further reduction of the false-positive rate are identified.

License Plate Recognition From Still Images and Video Sequences: A Survey

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> 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. </para>

Automatic license plate recognition

Automatic license plate recognition (LPR) plays an important role in numerous applications and a number of techniques have been proposed. However, most of them worked under restricted conditions, such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. In this study, as few constraints as possible on the working environment are considered. The proposed LPR technique consists of two main modules: a license plate locating module and a license number identification module. The former characterized by fuzzy disciplines attempts to extract license plates from an input image, while the latter conceptualized in terms of neural subjects aims to identify the number present in a license plate. Experiments have been conducted for the respective modules. In the experiment on locating license plates, 1088 images taken from various scenes and under different conditions were employed. Of which, 23 images have been failed to locate the license plates present in the images; the license plate location rate of success is 97.9%. In the experiment on identifying license number, 1065 images, from which license plates have been successfully located, were used. Of which, 47 images have been failed to identify the numbers of the license plates located in the images; the identification rate of success is 95.6%. Combining the above two rates, the overall rate of success for our LPR algorithm is 93.7%.