An efficient license plate character recognition algorithm based on shape context

2013 ◽  
Author(s):  
Yan Wan ◽  
Xiaotao Xu ◽  
Li Yao
Keyword(s):  
Character Recognition ◽  
Recognition Algorithm ◽  
License Plate ◽  
Shape Context

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.

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%.

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.

A Dynamic Sliding Window Based on Otsu Method for Binary License Plate and Character Recognition

2013 ◽  
Vol 397-400 ◽  
pp. 2301-2308
Author(s):  
Rui Jian ◽  
Jun Zhao
Keyword(s):  
Character Recognition ◽  
Recognition Rate ◽  
Grid Method ◽  
Sliding Window ◽  
Recognition Algorithm ◽  
Least Square ◽  
Support Vector ◽  
License Plate ◽  
License Plate Recognition ◽  
Dynamic Sliding Window

This paper is concerned with the problem of license plate recognition of vehicles. A recognition algorithm based on dynamic sliding window to binarize license plate characters is proposed. While a connected domain approach is presented to cope with the degradation characters. There are three steps to recognize the characters. First, the characters are classified by their features. Then, based on such classification a grid method is used to construct the feature vector. Finally, least square support vector machine is employed to recognize these characters. The test results show the high recognition rate and also illustrate the effectiveness of the proposed algorithm.

scholarly journals A Robust License Plate Detection and Character Recognition Algorithm Based on a Combined Feature Extraction Model and BPNN

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Fei Xie ◽  
Ming Zhang ◽  
Jing Zhao ◽  
Jiquan Yang ◽  
Yijian Liu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Intelligent Transportation System ◽  
Rapid Development ◽  
Contrast Ratio ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Detection ◽  
Extraction Model ◽  
Combined Feature

The rapid development of the license plate recognition technology has made great progress for its widespread uses in intelligent transportation system (ITS). This paper has proposed a novel license plate detection and character recognition algorithm based on a combined feature extraction model and BPNN (Backpropagation Neural Network) which is adaptable in weak illumination and complicated backgrounds. Firstly, a preprocessing is first used to strengthen the contrast ratio of original car image. Secondly, the candidate regions of license plate are checked to verify the true plate, and the license plate image is located accurately by the integral projection method. Finally, a new feature extraction model is designed using three sets of features combination, training the feature vectors through BPNN to complete accurate recognition of the license plate characters. The experimental results with different license plate demonstrate effectiveness and efficiency of the proposed algorithm under various complex backgrounds. Compared with three traditional methods, the recognition accuracy of proposed algorithm has increased to 97.7% and the consuming time has decreased to 46.1ms.

scholarly journals Research and Implementation of Vehicle Target Detection and Information Recognition Technology Based on NI myRIO

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1765 ◽  
Author(s):  
Hongliang Wang ◽  
Shuang He ◽  
Jiashan Yu ◽  
Luyao Wang ◽  
Tao Liu
Keyword(s):  
Information Extraction ◽  
Target Detection ◽  
Information Acquisition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Algorithm ◽  
License Plate ◽  
Logo Recognition ◽  
Vehicle Information ◽  
High Recognition Accuracy

A vehicle target detection and information extraction scheme based on NI (National Instruments) myRIO is designed in this paper. The vehicle information acquisition and processing method based on image recognition is used to design a complete vehicle detection and information extraction system. In the LabVIEW programming environment, the edge detection method is used to realize the vehicle target detection, the pattern matching method is used to realize the vehicle logo recognition, and the Optical Character Recognition (OCR) character recognition algorithm is used to realize the vehicle license plate recognition. The feasibility of the design scheme in this paper is verified through the actual test and analysis. The scheme is intuitive and efficient, with the high recognition accuracy.

An Improved Character Recognition Algorithm for License Plate Based on BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 202-207 ◽  
Author(s):  
Zhong Qu ◽  
Qing-li Chang ◽  
Chang-zhi Chen ◽  
Li-dan Lin
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Template Matching ◽  
Recognition Algorithm ◽  
License Plate ◽  
Recognition Method ◽  
Neural Network Method ◽  
Identification Rate ◽  
Advantages And Disadvantages ◽  
Network Method

License plate character recognition is the basis of automatic license plate recognition (LPR) and it plays an important role in LPR. In this paper, we considered the advantages and disadvantages of the neural network method and proposed an improved approach of character recognition for license plates. In our approach, firstly, license plates were segmented into character pictures by using the algorithm which combines the projection and morphology. Secondly, with a focus on each character picture, recognition results determined by the calculation of the new recognition algorithm were as a reflection of the different features of every kind of character image. Then, character image samples were classified according to different light environment and character type itself. Finally, we used extracted features vectors to train the BP (error back propagation) neural network with adding noise relatively. Due to the influence of environmental factors or character images themselves will bring font discrepancy, font slant, stroke connection and so on, compared with template matching recognition method, neural network method has relatively great space to enhance the recognition effect. In the experiment, we used 1000 license plates images that had been successfully located. Of which, 11800 character images have been successfully identified, and the identification rate of our new algorithm is 91.2%. The experiment results prove that the improved character recognition method is accurate and highly consistent.

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