License Plate Character Recognition Research Articles

Disentangled generation network for enlarged license plate recognition and a unified dataset

License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above challenges, the transportation management department generally introduces the enlarged license plate behind the rear of a vehicle. However, enlarged license plates have high diversity as they are non-standard in position, size, and style. Furthermore, the background regions contain a variety of noisy information which greatly disturbs the recognition of license plate characters. In this work, we address the enlarged license plate recognition problem and contribute a dataset containing 9342 images, which cover most of the challenges of real scenes. However, the created data are still insufficient to train deep methods of enlarged license plate recognition, and building large-scale training data is very time-consuming and high labor cost. To handle this problem, we propose a novel data generation framework based on the Disentangled Generation Network (DGNet), which disentangles the generation of enlarged license plate data into the text generation and background generation in an end-to-end manner to effectively ensure the generation diversity and integrity, for robust enlarged license plate recognition. Extensive experiments on the created dataset are conducted, and we demonstrate the effectiveness of the proposed approach in three representative text recognition frameworks.

Chinese License Plate Recognition System Based on Convolutional Neural Network

License Plate Recognition (LPR) technology has been developed for many years, but for the time being, LPR technology still has problems such as not being accurate enough in positioning and too long recognition time. Especially in China, the License Plate (LP) is made up of Chinese characters, alphabets, and numbers, in which the use of Chinese characters profoundly influences the accuracy of LPR. In this paper, a Convolutional Neural Network (CNN) is employed in LPR system, and the LPR system designed in this paper includes three parts: coarse LP positioning, precise LP positioning, and LP character recognition. The LPR system designed in this paper uses the functions in OpenCV to call the laptop camera and input the obtained images into the computer to achieve interframe recognition by setting the timer. Once the image is acquired, the approximate license plate position is determined by the cascade classifier. After this, the image is processed by adaptive binarization, followed by Connected Component Analysis (CCA), linear fitting, and left-right regression to precisely locate the LP. After the exact location of the LP has been determined, it will be fed into a CNN for character recognition. Simulation tests show that the LPR system implemented in this paper has some LPR capability.

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

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.

Spatial Transform Depthwise Over-Parameterized Convolution Recurrent Neural Network for License Plate Recognition in Complex Environment

Abstract Automatic license plate recognition (ALPR) system has been widely used in intelligent transportation and other fields. However, in complex environments such as vehicle sound source localization, poor illumination, or bad weather conditions, ALPR is still a challenging problem. Aiming at the problem, an end-to-end deep learning framework is developed based on depthwise over-parameterized convolution recurrent neural network for license plate character recognition. The proposed framework is composed as follows: (i) license plate correcting module based on spatial transformation network; (ii) feature extraction module based on depthwise over-parameterized convolution; (iii) sequence annotation module based on bidirectional long short-term memory; and (iv) regularized sequence decoding module based on connectionist temporal classification with maximum conditional entropy. Two open-source datasets of Chinese License Plate Datasets (SYSU) and Chinese City Parking Dataset (CCPD) are used to verify the performance of the algorithm. The proposed end-to-end framework can effectively rectify distorted and inclined license plates in spatial domain. It can recognize license plates without complex character segmentation process. Compared with some current state-of-art algorithms, the proposed algorithm achieved the best performance with the recognition accuracy of 96.31% and 88.31% based on the two datasets of SYSU and CCPD, respectively.

Improving Robustness of License Plates Automatic Recognition in Natural Scenes

Automatic license plate recognition plays an important role in intelligent transportation systems and is of great significance. However, at present, most current approaches are only concerned of license plate recognition under restrictive conditions, where the license plates are shot in a frontal view and under good light conditions. These approaches are not robust enough in real-world complex capture scenarios, such as uneven light condition or oblique shooting angle. In order to improve the robustness of recognizing license plates under complex capture scenarios, a robust license plate detection network (CA-CenterNet) is proposed in this paper, together with a segmentation-free network (CNNG) for the recognition of license plate characters. CA-CenterNet can detect not only the center of each license plate, but also four vectors pointing to the four corners of the corresponding license plate, regardless of the rotation and distortion of the license plates, which gives us the possibility to rectify the distorted license plates in the source images. Then, CNNG can accurately identify the characters in the detected license plates without character segmentation. Experimental results prove that our automatic license plate recognition system has good performance in real-world complex capture scenarios and outperforms current license plate recognition models.

License Plate Character Recognition using Convolutional Neural Network

Background: In the last decade, the number of registered vehicles has grown exponentially. With more vehicles on the road, traffic jams, accidents, and violations also increase. A license plate plays a key role in solving such problems because it stores a vehicle’s historical information. Therefore, automated license-plate character recognition is needed. Objective: This study proposes a recognition system that uses convolutional neural network (CNN) architectures to recognize characters from a license plate’s images. We called it a modified LeNet-5 architecture. Methods: We used four different CNN architectures to recognize license plate characters: AlexNet, LeNet-5, modified LeNet-5, and ResNet-50 architectures. We evaluated the performance based on their accuracy and computation time. We compared the deep learning methods with the Freeman chain code (FCC) extraction with support vector machine (SVM). We also evaluated the Otsu and the threshold binarization performances when applied in the FCC extraction method. Results: The ResNet-50 and modified LeNet-5 produces the best accuracy during the training at 0.97. The precision and recall scores of the ResNet-50 are both 0.97, while the modified LeNet-5’s values are 0.98 and 0.96, respectively. The modified LeNet-5 shows a slightly higher precision score but a lower recall score. The modified LeNet-5 shows a slightly lower accuracy during the testing than ResNet-50. Meanwhile, the Otsu binarization’s FCC extraction is better than the threshold binarization. Overall, the FCC extraction technique performs less effectively than CNN. The modified LeNet-5 computes the fastest at 7 mins and 57 secs, while ResNet-50 needs 42 mins and 11 secs. Conclusion: We discovered that CNN is better than the FCC extraction method with SVM. Both ResNet-50 and the modified LeNet-5 perform best during the training, with F measure scoring 0.97. However, ResNet-50 outperforms the modified LeNet-5 during the testing, with F-measure at 0.97 and 1.00, respectively. In addition, the FCC extraction using the Otsu binarization is better than the threshold binarization. Otsu binarization reached 0.91, higher than the static threshold binarization at 127. In addition, Otsu binarization produces a dynamic threshold value depending on the images’ light intensity. Keywords: Convolutional Neural Network, Freeman Chain Code, License Plate Character Recognition, Support Vector Machine

License Plate Characters Recognition Using Color, Scale, and Rotation Independent Features

This paper proposes a color, scale, and rotation independent approach for recognizing License Plate (LP) characters written in English. We extract features based on geometrical properties of an LP character, like the number of perimeters, perimeter angles, different characteristics points on the perimeter, etc. These features are next used to generate a characteristic encoding of the character. The generated encoding based on color, scale, and rotation independent geometrical properties is then used to identify an LP character independent of its color, scale, and rotational angle. We have evaluated our proposed approach using images from the publicly available Media Lab LP dataset, and a recognition rate of 98.29% was obtained. The processing time for an input image of size 200 × 100 pixel was found to be 0.3 ms. The recognition rate and low processing time compare favorably with results published in the literature by other LP characters recognition techniques. Further, unlike other published techniques, our approach places no restriction on the size, color, or the number of characters in LP, nor is our method restricted to any particular LP design or the LP of any specific country or region.

Edge-AI-Based Real-Time Automated License Plate Recognition System

The rapid development of urban intelligence has turned intelligent transport system (ITS) development into a primary goal of traffic management. Automated license plate recognition (ALPR) for moving vehicles is a core aspect of ITS. Most ALPR systems send images back to a server for license plate recognition. To reduce delays and bandwidth use during image transmission, this study proposes an edge-AI-based real-time ALPR (ER-ALPR) system, in which an AGX XAVIER embedded system is embedded on the edge of a camera to achieve real-time image input to an AGX edge device and to enable real-time automatic license plate character recognition. To assess license plate characters and styles in a realistic setting, the proposed ER-ALPR system applies the following approaches: (1) image preprocessing; (2) You Only Look Once v4-Tiny (YOLOv4-Tiny) for license plate frame detection; (3) virtual judgment line for determining whether a license plate frame has passed; (4) the proposed modified YOLOv4 (M-YOLOv4) for license plate character recognition; and (5) a logic auxiliary judgment system for improving license plate recognition accuracy. We tested the proposed ER-ALPR system in selected real-life test environments in Taiwan. In experiments, the proposed ER-ALPR system achieved license plate character recognition rates of 97% and 95% in the day and at night, respectively. Through the AGX system, the proposed ER-ALPR system achieves a high recognition rate at a low computational cost.

Blpnet: A New Dnn Model and Bengali Ocr Engine for Automatic License Plate Recognition

The development of the Automatic License Plate Recognition (ALPR) system has received much attention for the English license plate. However, despite being the sixth largest population around the world, no significant progress can be tracked in the Bengali language countries or states for the ALPR system addressing their more alarming traffic management with inadequate road-safety measures. This paper reports a computationally efficient and reasonably accurate Automatic License Plate Recognition (ALPR) system for Bengali characters with a new end-to-end DNN model that we call Bengali License Plate Network(BLPnet). The cascaded architecture for detecting vehicle regions prior to vehicle license plate (VLP) in the model is proposed to eliminate false positives resulting in higher detection accuracy of VLP. Besides, a lower set of trainable parameters is considered for reducing the computational cost making the system faster and more compatible for a real-time application. With a Computational Neural Network (CNN)based new Bengali OCR engine and word-mapping process, the model is characters rotation invariant, and can readily extract, detect and output the complete license plate number of a vehicle. The model feeding with17 frames per second (fps) on real-time video footage can detect a vehicle with the Mean Squared Error (MSE) of 0.0152, and the mean license plate character recognition accuracy of 95%. While compared to the other models, an improvement of 5% and 20% were recorded for the BLPnetover the prominent YOLO-based ALPR model and the Tesseract model for the number-plate detection accuracy and time requirement, respectively.

Hybrid Metaheuristics Based License Plate Character Recognition in Smart City

Recent technological advancements have been used to improve the quality of living in smart cities. At the same time, automated detection of vehicles can be utilized to reduce crime rate and improve public security. On the other hand, the automatic identification of vehicle license plate (LP) character becomes an essential process to recognize vehicles in real time scenarios, which can be achieved by the exploitation of optimal deep learning (DL) approaches. In this article, a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition (HMODL-ALPCR) technique has been presented for smart city environments. The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them. For effective LP detection process, mask regional convolutional neural network (Mask-RCNN) model is applied and the Inception with Residual Network (ResNet)-v2 as the baseline network. In addition, hybrid sunflower optimization with butterfly optimization algorithm (HSFO-BOA) is utilized for the hyperparameter tuning of the Inception-ResNetv2 model. Finally, Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs. The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.

Automatic License Plate Recognition System for Vehicles Using a CNN

Automatic License Plate Recognition (ALPR) systems are important in Intelligent Transportation Services (ITS) as they help ensure effective law enforcement and security. These systems play a significant role in border surveillance, ensuring safeguards, and handling vehicle-related crime. The most effective approach for implementing ALPR systems utilizes deep learning via a convolutional neural network (CNN). A CNN works on an input image by assigning significance to various features of the image and differentiating them from each other. CNNs are popular for license plate character recognition. However, little has been reported on the results of these systems with regard to unusual varieties of license plates or their success at night. We present an efficient ALPR system that uses a CNN for character recognition. A combination of pre-processing and morphological operations was applied to enhance input image quality, which aids system efficiency. The system has various features, such as the ability to recognize multi-line, skewed, and multi-font license plates. It also works efficiently in night mode and can be used for different vehicle types. An overall accuracy of 98.13% was achieved using the proposed CNN technique.

Research on Car License Plate Recognition Based on Improved YOLOv5m and LPRNet

The application of license plate recognition technology is becoming more and more extensive. In view of the current practical requirements for the recognition accuracy and real-time performance of license plate recognition system in complex scenes, the existing target detection methods and license plate recognition methods are studied, and a car license plate recognition method based on improved YOLOv5m and LPRNet model is proposed. On the basis of studying the YOLOv5m algorithm and the image features of the car license plate, the YOLOv5m algorithm is improved from three aspects: the K-means++ algorithm is used to improve the matching degree between the anchor frame and the detection target, the DIOU loss function is used to improve the NMS method, and the feature map with 20×20 is removed to reduce the number of detection layers. A lightweight LPRNet network is used to realize license plate character recognition without character segmentation. Combining the improved YOLOv5m algorithm with LPRNet network, a license plate recognition system based on IYOLOv5m-LPRNet model is designed. The experimental results show that the average recognition accuracy of license plates in front, tilt, night and strong light interference scenes is more than 98%; Compared with the models of YOLOv3-LPRNet, YOLOv4-LPRNet, YOLOv5s-LPRNet and YOLOv5m-LPRNet, the recognition accuracy and recall rate of this method are improved, reaching 99.49% and 98.79% respectively; The mAP of this method is also the highest, reaching 98.56%; In terms of recognition speed, this method is also faster than the other four methods, and the number of pictures processed per second is increased by 5 compared with the YOLOv5m-LPRNet model. Therefore, the improved license plate recognition method in this paper performs well in robustness and speed.

A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry

Automatic License Plate Recognition (ALPR) is a widely used technology. However, due to the influence of complex environmental factors, recognition accuracy and speed of license plate recognition have been challenged and expected. Aiming to construct a sufficiently robust license plate recognition model, this study adopted multitask learning in the license plate detection stage, used the convolutional neural networks of single-stage detection, RetinaFace, and MobileNet, as approaches to license plate location, and completed the license plate sampling through the calculation of license plate skew correction. In the license plate character recognition stage, the Convolutional Recurrent Neural Network (CRNN) integrated with the loss function of the CTC model was employed as a segmentation-free and highly robust method of license plate character recognition. In this study, after the license plate recognition model, DLPR, trained the PVLP dataset of vehicle images provided by company A in Taiwan’s data processing industry, it performed tests on the PVLP dataset, indicating that its precision was 98.60%, recognition accuracy was 97.56%, and recognition speed was FPS > 21. In addition, according to the tests on the public AOLP dataset of Taiwan’s vehicles, its recognition accuracy was 97.70% and recognition speed was FPS > 62. Therefore, not only can the DLPR model be applied to the license plate recognition of real-time image streams in the future, but also it can assist the data processing industry in enhancing the accuracy of license plate recognition in photos of traffic violations and the performance of traffic service operations.

Super-Resolved Recognition of License Plate Characters

Object detection and recognition are crucial in the field of computer vision and are an active area of research. However, in actual object recognition processes, recognition accuracy is often degraded due to resolution mismatches between training and test image data. To solve this problem, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique that improves object recognition accuracy. In detail, we collected a number of license plate training images through web-crawling and artificial data generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to image flips. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on representative test images and confirmed that the proposed super-resolution technique improves the accuracy of character recognition. For character recognition with the 4× magnification, the proposed method remarkably increased the mean average precision by 49.94% compared to the existing state-of-the-art method.

Full depth CNN classifier for handwritten and license plate characters recognition

Character recognition is an important research field of interest for many applications. In recent years, deep learning has made breakthroughs in image classification, especially for character recognition. However, convolutional neural networks (CNN) still deliver state-of-the-art results in this area. Motivated by the success of CNNs, this paper proposes a simple novel full depth stacked CNN architecture for Latin and Arabic handwritten alphanumeric characters that is also utilized for license plate (LP) characters recognition. The proposed architecture is constructed by four convolutional layers, two max-pooling layers, and one fully connected layer. This architecture is low-complex, fast, reliable and achieves very promising classification accuracy that may move the field forward in terms of low complexity, high accuracy and full feature extraction. The proposed approach is tested on four benchmarks for handwritten character datasets, Fashion-MNIST dataset, public LP character datasets and a newly introduced real LP isolated character dataset. The proposed approach tests report an error of only 0.28% for MNIST, 0.34% for MAHDB, 1.45% for AHCD, 3.81% for AIA9K, 5.00% for Fashion-MNIST, 0.26% for Saudi license plate character and 0.97% for Latin license plate characters datasets. The license plate characters include license plates from Turkey (TR), Europe (EU), USA, United Arab Emirates (UAE) and Kingdom of Saudi Arabia (KSA).

Research on Chinese License Plate Recognition Algorithm Based on Convolution Neural Network

As an important part of intelligent traffic management, license plate recognition has very important research value. In order to solve the problem that the location of traditional license plate detection methods is not accurate and the detection results are easily affected by the environment, this paper proposes a license plate detection algorithm based on Faster R-CNN and Inception ResNet_v2, which realizes accurate license plate detection on VOC license plate data set by means of transfer learning. The effective extraction of a single character is realized by using the pixel statistics method in the detection results, and then the mLeNet5 convolution neural network model is designed to recognize the single character. The experimental results show that the algorithm can achieve efficient and accurate recognition of license plate characters with occlusion and angle tilt.

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.