scholarly journals License plate identification and recognition in a non-standard environment using neural pattern matching

2021 ◽  
Author(s):  
Imran Shafi ◽  
Imtiaz Hussain ◽  
Jamil Ahmad ◽  
Pyoung Won Kim ◽  
Gyu Sang Choi ◽  
...  
Keyword(s):  
Character Recognition ◽  
Detection Algorithm ◽  
Input Image ◽  
Recognition Algorithm ◽  
Detection Accuracy ◽  
License Plate ◽  
Recognition Mechanism ◽  
Robust Detection ◽  
Feature Representations ◽  
Recognition Efficiency

AbstractNon-standard license plates are a part of current traffic trends in Pakistan. Private number plates should be recognized and, monitored for several purposes including security as well as a well-developed traffic system. There is a challenging task for the authorities to recognize and trace the locations for the certain number plate vehicle. In a developing country like Pakistan, it is tough to have higher constraints on the efficiency of any license plate identification and recognition algorithm. Character recognition efficiency should be a route map for the achievement of the desired results within the specified constraints. The main goal of this study is to devise a robust detection and recognition mechanism for non-standard, transitional vehicle license plates generally found in developing countries. Improvement in the character recognition efficiency of drawn and printed plates in different styles and fonts using single using multiple state-of-the-art technologies including machine-learning (ML) models. For the mentioned study, 53-layer deep convolutional neural network (CNN) architecture based on the latest variant of object detection algorithm-You Only Look Once (YOLOv3) is employed. The proposed approach can learn the rich feature representations from the data of diversified license plates. The input image is first pre-processed for quality improvement, followed by dividing it into suitable-sized grid cells to find the correct location of the license plate. For training the CNN, license plate characters are segmented. Lastly, the results are post-processed and the accuracy of the proposed model is determined through standard benchmarks. The proposed method is successfully tested on a large image dataset consisting of eight different types of license plates from different provinces in Pakistan. The proposed system is expected to play an important role in implementing vehicle tracking, payment for parking fees, detection of vehicle over-speed limits, reducing road accidents, and identification of unauthorized vehicles. The outcome shows that the proposed approach achieves a plate detection accuracy of 97.82% and the character recognition accuracy of 96%.

scholarly journals License Plate Recognition

2020 ◽  
pp. 500-504
Author(s):  
B. Likith Ram ◽  
P. Naga Sai Teja ◽  
Y. Sai Avinash Kumar ◽  
Ch. Sai Raj
Keyword(s):  
Character Recognition ◽  
Detection Algorithm ◽  
Input Image ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Canny Edge ◽  
Plate Area ◽  
Canny Edge Detection Algorithm

<p>License Plate Recognition (LPR) system is an application of computer vision and image processing technology that takes video of vehicles and take the vehicle frame as input image and by extracting their number plate from whole vehicle image, it displays the number plate information into text. The overall accuracy and efficiency of whole LPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The approach used to segment the image is bilateral filtering algorithm and canny edge detection algorithm. Then we predict the license plate from processed image using py–tesseract OCR and match the retrieved text which is vehicle number plate with database. Finally we get the details of the particular vehicle from the database.</p>

scholarly journals Citrus Huanglongbing Recognition Algorithm Based on CKMOPSO

2021 ◽  
Vol 15 (4) ◽  
pp. 1-11
Author(s):  
Hui Wang ◽  
Tie Cai ◽  
Wei Cao
Keyword(s):  
Particle Swarm Optimization Algorithm ◽  
Kriging Model ◽  
Recognition Algorithm ◽  
Image Features ◽  
Detection Accuracy ◽  
Recognition Method ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Citrus Huanglongbing ◽  
Recognition Efficiency

In view of the similarity of characteristics between the features of the disease images and the large dimension, and the features correlation of the disease images, this will lead to the generation of feature redundancy, and will introduce a serious impact on the recognition efficiency and accuracy of citrus Huanglongbing. In addition, they have the defects of high cost of detection algorithms and low detection accuracy. This will occur in the image cutting feature extraction stage, so this paper uses the citrus Huanglongbing recognition algorithm based on kriging model simplex crossover local based search Multi-objective particle swarm optimization algorithm(CKMOPSO) selects feature vectors with strong classification capabilities from the original disease image features, experimental results show that this is an effective recognition method.

scholarly journals License Plate Recognition with Feature Salience and Neural Network

2019 ◽  
Vol 8 (10) ◽  
pp. 2985-2990
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Core Component ◽  
Character Extraction ◽  
Recognition Result

Character recognition algorithm is considered as a core component of License Plate Recognition (LPR) systems. Numerous methods for License Plate (LP) recognition have been developed in recent years. However, most of them are not advanced enough to recognize in complex background and still demand improvement. This paper introduces a novel system for LPR by analyzing vehicle images. Accurate segmentation of license plate and character extraction from the plate is accomplished. In the plate segmentation module, Hough transform is put forwarded to identify plate edges using line segments. Radon transform adjusts the skew between LP and the viewer, thereby improve the recognition result. Four features are extracted from the LP image, and best features are selected using feature-salience theory. Histogram projection is performed horizontally and vertically to isolate individual characters in the LP. Finally, Back Propagation Neural Network (BPNN) is used to identify the characters present in the LP. From experimental results, it is evident that the proposed system can recognize LP more efficiently and establish a good background for future advancements in LPR.

A Study of Self-Adaptive Front Vehicle Recognition Algorithm Based on Multi-Features

2014 ◽  
Vol 945-949 ◽  
pp. 1815-1819
Author(s):  
Mei Hua Xu ◽  
Chen Jun Xia ◽  
Huai Meng Zheng
Keyword(s):  
Vehicle Detection ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
License Plate ◽  
Improved Method ◽  
Vehicle Recognition ◽  
Potential Risks ◽  
Self Adaptive

With the development of intelligent driving technology, recognition the vehicle in front of our cars became the hotspot in the field of intelligent driving research. This paper presents a self-adaptive front vehicle recognition algorithm with some unique improved method on the basis of analyzing and comparing the popular vehicle detection algorithm of domestic and foreign. Using the gray feature, vehicle shadow feature, taillights feature, license plate color domain feature and other features, the recognition algorithm can detect the vehicle in front of cars effectively, find out the safe passage area and avoid the potential risks. Finally, the feasibility of the algorithm is verified by experiment results with MATLAB tools.

Character Pattern Recognition Algorithm Based on Principal Component Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 5313-5318
Author(s):  
Feng Xian Tang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Character Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Algorithm ◽  
License Plate ◽  
Specific Work ◽  
Advantages And Disadvantages

With the help of the study on mathematical theory and its progress and the development of the computer techniques, digital image processing technology has more and more been applied in each field. The pattern recognition judges unknown things by substituting machine for human eyes, which has a high application value. Thus, it becomes the major branch in image processing fields. The character recognition technology has developed rapidly because of its broad application prospect. Until now, it has been applied successfully in OCR and vehicle license plate recognition. However, it has certain difficulty for the pattern recognition to meet the specific requirements related to specific work scenes. This essay discusses several Eigen value selecting approaches and analyzes the advantages and disadvantages of each. For the template matching methods with penalty factors, in design, character recognition algorithm based on the principal component analysis is realized where scattering matrix between classes is as produced matrix.

scholarly journals An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1358
Author(s):  
Yan Liu ◽  
Jingwen Wang ◽  
Tiantian Qiu ◽  
Wenting Qi
Keyword(s):  
High Speed ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Input Image ◽  
Research Field ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Intelligent Traffic System ◽  
Blurred Image ◽  
Blurred Images

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.

scholarly journals An Improved Discrete Cosine Transformation Block Based Scheme for Copy-move Image Forgery Detection

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Abdulraheem Hassanat Oyiza ◽  
Mohd Aizaini Maarof
Keyword(s):  
Detection Algorithm ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Image Block ◽  
Discrete Cosine Transformation ◽  
Image Forgery ◽  
Robust Detection ◽  
Image Forgery Detection ◽  
Dct Coefficients ◽  
Block Based

Copy-moved forgery is a common method to manipulate images. Several attempts of image forgery have been discovered and involves a region been duplicated and copied and pasted on another region of the same image in other to achieve selfish gain. Generally, there are two classification of copy-move forgery detection technique such as the block-based and key point-based. The block-based division is mostly used and divides image into blocks during the stage of image pre-processing before features are extracted, whereas key-point based technique skips the division of image into blocks and directly extracts different local feature from the image. In this paper, we review various block based and key point approach which has been proposed by various researchers. There is a problem of achieving a balance between improving the detection accuracy and having minimal computational complexity. The proposed technique is based on an improved DCT based copy-move image forgery detection (IDB-CFD), which involves using an octagonal block to reduce the number of features for matching, thereby improving detection accuracy while having minimal complexity. The analysis of this work as compared to previous proposed works which is based on a robust detection algorithm for copy-move image forgery (RDA-CF) and involves using circle block to reduce the number of features, results show that previous work represents about 79% of the quantized DCT coefficients on each image block and this proposed work represents about 85% of quantized DCT coefficients, therefore, recovery of about 6% more features using the IDB-CFD technique was observed as the improvement over the previously proposed RDA-CF.

scholarly journals Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis

2021 ◽  
Vol 11 (22) ◽  
pp. 10614
Author(s):  
Musa Al-Yaman ◽  
Haneen Alhaj Mustafa ◽  
Sara Hassanain ◽  
Alaa Abd AlRaheem ◽  
Adham Alsharkawi ◽  
...  
Keyword(s):  
Character Recognition ◽  
Detection Accuracy ◽  
License Plate ◽  
License Plate Recognition ◽  
Size Estimation ◽  
Main Challenge ◽  
Detection Stage ◽  
Overall Performance ◽  
Candidate Regions ◽  
Automatic License Plate Recognition

The main challenge of automatic license plate recognition (ALPR) systems is that the overall performance is highly dependent upon the results of each component in the system’s pipeline. This paper proposes an improved ALPR system for the Jordanian license plates. Ceiling analysis is carried out to identify potential enhancements in each processing stage of a previously reported ALPR system. Based on the obtained ceiling analysis results, several enhancements are then suggested to improve the overall performance of the system under study. These improvements are (i) vertical-edge histogram analysis and size estimation of the candidate regions in the detection stage and (ii) de-rotation of the misaligned license plate images in the segmentation unit. These enhancements have resulted in significant improvements in the overall system performance despite a <1% increase in the execution time. The performance of the developed ALPR is assessed experimentally using a dataset of 500 images for parked and moving vehicles. The obtained results are found to be superior to those reported in equivalent systems, with a plate detection accuracy of 94.4%, character segmentation accuracy of 91.9%, and character recognition accuracy of 91.5%.

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