License Plate Character Research Articles

Retracted: Image Recognition Technology Based on Machine Learning

With the development of machine learning for decades, there are still many problems unsolved, such as image recognition and location detection, image classification, image generation, speech recognition, natural language processing and so on. In the field of deep learning research, the research on image classification has always been the most basic, traditional and urgent research direction. At the same time, computer intelligent image recognition technology is also conducive to gradually better respond to the development of international indicators, and promote the development and progress of various fields. Therefore, image processing technology based on machine learning has been widely used in feature image, classification, segmentation and recognition, and is a hot spot in various fields. However, due to the complexity of video images and the distribution of objects in different application backgrounds, the classification accuracy becomes important and difficult. In the paper transportation industry, image recognition technology is applied to license plate recognition to extract license plate from complex background, segment license plate characters and recognize characters, and construct a machine learning non license plate automatic generation algorithm, which may improve the efficiency of non license plate recognition. The diversity and high generation speed of license plate training sample set can achieve the purpose of effectively training strong classifier. By using genetic algorithm to optimize BP neural network to classify license plate information, the anti-interference ability and license plate recognition accuracy are improved to a certain extent.

Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to identify and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully detects LP and recognizes multi-style Arabic characters with rates of 96% and 97.872% respectively under different conditions.

A Method of License Plate Location and Character Recognition based on CNN

At the present time, the economy continues to flourish, and private cars have become the means of choice for most people. Therefore, the license plate recognition technology has become an indispensable part of intelligent transportation, with research and application value. In recent years，the convolution neural network for image classification is an application of deep learning on image processing. This paper proposes a strategy to improve the YOLO model by studying the deep learning convolutional neural network (CNN) and related target detection methods, and combines the OpenCV and TensorFlow frameworks to achieve efficient recognition of license plate characters. The experimental results show that target detection method based on YOLO is beneficial to shorten the training process and achieve a good level of accuracy.

Development of Iraqi License Plate Recognition System based on Canny Edge Detection Method

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the road in all the sections of the country. Vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the developing system is consist of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny Edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to recognize and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully identified and recognized multi_style Iraqi license plates using different image situations and it was evaluated based on different metrics performance, achieving an overall system performance of 91.99%. This results shows the effectiveness of the proposed method compared with other existing methods, whose average recognition rate is 86% and the average processing time of one image is 0.242s which proves the practicality of the proposed method.

Detection and Identification Indonesia License Plate Using Background Subtraction Based on Area

The increasing number of vehicles, especially four-wheeled vehicles in big cities, raises traffic problems, including toll roads. With a fast transaction process at the entrance of the toll road, it can reduce the queue of traffic congestion. For this reason, it is necessary to use a vehicle number character recognition image processing application. In this study, an application for license plate character recognition was made on vehicle images starting from the plate detection process using background subtraction. Next is the segmentation process of each character and feature extraction process using area-based features extraction. For the character recognition process, use the K-Nearest Neighbor method. The data used in this study were 50 vehicle images with each of them having 3 distances namely 50 cm, 75 cm and 100 cm. The results of this research trial showed the best level of accuracy for the detection of license plate locations is 94%, the best accuracy of segmentation per character is 71.79% and 82.59% is the best accuracy of character recognition.

EDF‐LPR: a new encoder–decoder framework for license plate recognition

Although automatic license plate recognition (ALPR) has been studied for decades, the final recognition result can be accurate only if the license plate is detected and the standard format is unambiguous. However, since an image may contain license plates with different formats and scales, license plate detection and standard format classification may fail. In this study, a new ALPR codec framework named EDF-LPR is presented. As for the encoder, at the first stage, candidate license plate characters are detected and recognised directly without considering the format of license plate, and candidate regions of characters are extracted by density-based spatial clustering of applications with noise-like algorithm; at the second stage, poor regions are processed by tilt correction and scale normalisation to obtain more accurate candidate characters. As for the decoder, a sequence learning model is trained to convert each unordered coded sequence into a sequence composed of marks that indicate a way to construct the final result string. Experiments are designed to evaluate the performance of EDF-LPR on both detection rate and recognition rate. The experimental results on public datasets show that the detection rate and recognition rate are 99.51 and 95.3%, respectively, at about 40 fps.

License Plate Image Generation using Generative Adversarial Networks for End-To-End License Plate Character Recognition from a Small Set of Real Images

License Plate Character Recognition (LPCR) is a technology for reading vehicle registration plates using optical character recognition from images and videos, and it has a long history due to its usefulness. While LPCR has been significantly improved with the advance of deep learning, training deep networks for LPCR module requires a large number of license plate (LP) images and their annotations. Unlike other public datasets of vehicle information, each LP has a unique combination of characters and numbers depending on the country or the region. Therefore, collecting a sufficient number of LP images is extremely difficult for normal research. In this paper, we propose LP-GAN, an LP image generation method, by applying an ensemble of generative adversarial networks (GAN), and we also propose a modified lightweight YOLOv2 model for an efficient end-to-end LPCR module. With only 159 real LP images available online, thousands of synthetic LP images were generated by using LP-GAN. The generated images not only looked similar to real ones, but they were also shown to be effective for training the LPCR module. As a result of performance tests with 22,117 real LP images, the LPCR module trained with only the generated synthetic dataset achieved 98.72% overall accuracy, which is comparable to that of training with a real LP image dataset. In addition, we improved the processing speed of LPCR about 1.7 times faster than that of the original YOLOv2 model by using the proposed lightweight model.

An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. 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. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). 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. 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. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions.

Chord-Length Shape Features for License Plate Character Recognition

Despite their recognized merits in terms of discrimination, compactness, and robustness, chord-length shape features have not received a great deal of attention in the literature on license plate recognition. In this paper, we present an innovative k nearest neighbors (kNN) approach for license plate detection and recognition, where a new low-dimensional descriptor that incorporates shape information of plate characters is formed from a finite set of established 1D chord-length signatures. When evaluated on a dataset incorporating a relatively large and diverse collection of plate image data, the proposed approach delivers promising results that compare favorably with those reported in the literature, without sacrificing computational efficiency or stability.

Representation learning in a deep network for license plate recognition

The goal of license plate recognition (LPR) is to read the license plate characters. Due to image degradation, there are many difficulties in the way of achieving this goal. In this paper, the proposed method recognizes the license plate characters without employing the traditional segmentation and binarization techniques. This method uses a deep learning algorithm and tries to achieve better learning experience by engaging a multi-task learning algorithm based on sharing features. The features of license plate characters are extracted by a deep encoder-decoder network, and transferred to 8 parallel classifiers for recognition. To evaluate the current work, a database of 11,000 license plate images, collected from a currently working surveillance system installed on a dual carriageway, is employed. The proposed method achieved the correct character recognition rate of 96% for 4000 test images that is acceptable in comparison to the competing methods.

Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non-uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN) based recognition called OKM-CNN model. The proposed OKM-CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM-CNN model over other compared methods in a considerable way.

A Robust License Plate Recognition Model Based on Bi-LSTM

License plate detection and recognition are still important and challenging tasks in natural scenes. At present, most methods have favorable effect on license plate recognition under restrictive conditions, and most of such license plates are shot under good angle and light conditions. However, for license plates under non-restrictive conditions, such as dark, bright, rotated conditions etc. from the Chinese City Parking Dataset (CCPD), the performance of some methods of license plate recognition will be significantly reduced. In order to improve the accuracy of license plate recognition under unrestricted conditions, a robust license plate recognition model is proposed in this paper, which mainly includes license plate feature extraction, license plate character localization, and feature extraction of characters. First of all, the model can activate the regional features of characters and fully extract the character features of license plates. Then locate each license plate character through Bi-LSTM combined with the context location information of license plates. Finally, 1D-Attention is adopted to enhance useful character features after Bi-LSTM positioning, and reduce useless character features to realize effective acquisition of character features of license plates. A large number of experimental results demonstrate that the proposed algorithm has good performance under unrestricted conditions, which proves the effectiveness and robustness of the model. In CCPD-Base, CCPD-DB, CCPD-FN, CCPD-Tilt, CCPD-Weather, CCPD-Challenge and other sub-datasets, the recognition rates reach 99.3%, 98.5%, 98.6%, 96.4%, 99.3% and 86.6% respectively.

Research and implementation of license plate recognition based on android platform

This paper studies and optimizes license plate location and recognition in license plate recognition. A license plate recognition system based on Android platform is designed and implemented. Opencv and Tesseract OCR are integrated in Android studio environment. The license plate number is located by combining Laplace algorithm and HSV model. On the basis of fully understanding the principle of Tesseract OCR recognition, a large number of training pictures are generated by license plate number simulation generator, and license plate character library is generated by using jtessboxeditor tool, which realizes offline recognition of license plate number.

Rendszámtábla azonosító rendszer tervezése Python és OpenCV használatával

This article describes the structure of an implementation of a license plate identification system capable of reading the characters of a license plate from a digital image. It also collects the license plate detecting and character recognizing methods mentioned in the article, and we can read about rules regarding hungary's registered license plates as well. We can gain insight into the world of computer vision, as well as understand what image manipulation procedures a digital image must go through in order to extract characters from it. The system was written in Python and also used methods provided by OpenCV, as well as the Python-Tesseract optical character recognition tool to identify license plate characters.

License Plate Character Segmentation Algorithm Based on Improved Regression Model

Many character segmentation algorithms in license plate recognition system are not very good for segmentation of complex characters, and there is a phenomenon of character sticking, which results in slow recognition speed and not very good recognition effect of the license plate with complex characters. For such problems, a segmentation algorithm based on the improved regression model on statistical characteristics is presented. Compared with other algorithms, experimental results show that less time is consumed and segmentation effect is better. The recognition rate of license plate can be improved effectively.

Automated Toll Booth using Morphological Edge Detection Algorithm

In order to reduce the traffic jam, save time and to diminish the money loss of 300 crores/year, an automated system is proposed; The intelligent traffic control management systems which explains the installation of automation in toll plazas, which is a step towards improving the monitoring of vehicles. The main purpose of this arrangement is to implement a system, which automatically identifies an approaching vehicle and records the vehicles number and time. If the vehicle belongs to an authorized person, the toll gate opens automatically and a predetermined amount is deducted from the user account, which leads to reduce the Traffic clogging at toll plazas. The vehicle number plate recognition is done only for authorized members. The proposed model consists of image digitization, edge detection, character detection and recognition with the payment transaction. The proposed method uses morphological edge detection , for the system to identify the character recognition and it uses template matching; it is done for license plates of 6 characters. The license plate character recognition system can be used in vehicle check -in and checkout monitoring system in hotels, malls and can be used to track vehicles on normal roads other than toll booths to avoid thefts.

Research on License Plate Image Segmentation and Intelligent Character Recognition

With the accumulation of people’s wealth and the improvement of purchasing power, more and more people are buying cars as a means of travel. Walking and cycling of the past have now become a car trip. License plate recognition technology is especially important in intelligent transportation systems. It has been widely used in large shopping malls or supermarket parking lots, highway toll stations, speeding violation supervision and other fields. However, the accuracy and efficiency of license plate image recognition are insufficient. To solve the above problems, we propose a license plate character recognition method based on local HOG and layered LBP feature fusion from the perspectives of image pre-processing, license location, characters’ segmentation and recognition. First, pre-processing the license image area by highlighting the license plate image; then, the license plate is positioned based on wavelet decomposition and brightness moment; next the tilted license plate image is corrected, the license plate frame is adjusted, and characterization is performed by using the improved projection method based on the fact that the projection of the character is a single peak or a double peak. Finally, the local HOG and hierarchical LBP feature fusion methods are used to identify the license characters. The results show that the license plate’s character recognition rate of the proposed method reaches 99.71%, and the time taken is small. This not only improves the character recognition rate, but also saves recognition time. The results show that the method has important practical significance in license plates’ recognition.

Locating multiple license plates using scale, rotation, and colour‐independent clustering and filtering techniques

A license plate (LP) can help identify a motor vehicle. However, no common standard for LP exists across countries, and even within a country, significant LP variations are observed. In addition, environmental factors cause uncontrolled plate and character variations. It is, therefore, a challenge to design a robust and universal license plate recognition (LPR) system which works for multiple countries, for different types of vehicles, and for different styles of LPs. This study presents a novel approach for locating LP based on the use of multiple clustering and filtering techniques applied to the geometrical properties of LP characters. The proposed approach is independent of the size, rotation, and colour of the LP and can be used to locate single or multiple LP of different styles of different vehicles and of different countries. The approach has been validated using the standard Media-lab and application-oriented license plate (AOLP) datasets as well as on datasets of vehicles from other countries. The approach achieved an average success ratio of 93.42% for locating LPs from both the Media-lab and the AOLP dataset and is higher than the results of previously published methods which evaluated their performance over the same datasets.

Vehicle license plate recognition method based on deep convolution network in complex road scene

The license plate robust recognition algorithm in complex road scene has both theoretical and practical values. The existing license plate recognition algorithm can achieve better recognition results under ideal road scenes such as moderate light intensity, good shooting angle, and clear license plate target, but in complex road scenes such as fast speed, blurred aging of license plates, and low illumination such as rainy days, the effectiveness of the license plate recognition algorithm still needs to be improved. Based on the realistic requirements of license plate recognition algorithm and in-depth analysis of the principle of deep convolution network, we designed a deep convolution network for Chinese characters, letters, and numbers in the license plate to automatically learn the essential features of the image to make up for the limitation of the artificial feature recognition of the traditional license plate recognition algorithm. At the same time, according to the convolution kernel, downsampling, and nonlinear operation of the deep convolution network, the nonlinear abstraction ability of the license plate character feature is improved. The experimental results show that the proposed method can quickly and accurately identify the license plate character in complex road scenes. The recognition accuracy is better than the existing license plate recognition algorithm, which improves the accuracy of license plate recognition and achieves an ideal license plate recognition effect.

Barrier Access Control Using Sensors Platform and Vehicle License Plate Characters Recognition.

The paper proposes a sensors platform to control a barrier that is installed for vehicles entrance. This platform is automatized by image-based license plate recognition of the vehicle. However, in situations where standardized license plates are not used, such image-based recognition becomes non-trivial and challenging due to the variations in license plate background, fonts and deformations. The proposed method first detects the approaching vehicle via ultrasonic sensors and, at the same time, captures its image via a camera installed along with the barrier. From this image, the license plate is automatically extracted and further processed to segment the license plate characters. Finally, these characters are recognized with the help of a standard optical character recognition (OCR) pipeline. The evaluation of the proposed system shows an accuracy of 98% for license plates extraction, 96% for character segmentation and 93% for character recognition.