License Plate Location Algorithm Research Articles

Feature extraction for license plate location based onL0-norm smoothing

AbstractWe propose a simple feature extraction algorithm for license plate location, which can reduce the occurrence of pseudo-licenses significantly. Our scheme arises from a novelL0-norm image smoothing, in which the multiple local textures in the complex backgrounds can be suppressed remarkably without changing the structures and edges of the license objects. Due to this “edgeaware” property, we then combine a feature filtering with an efficient binarized image, a simple multi-scale image analysis algorithm, to remove the potential false license plates. Finally, we extract license plates with a projection method. Experimental results show the proposed method provides a flexible and powerful way to the license plate location in complex backgrounds.

A new algorithm of vehicle license plate location based on convolutional neural network

A new algorithm of vehicle license plate location based on convolutional neural network

The license plate recognition system based on improved algorithm

The research of this paper is based on the license plate location algorithm and Radon transform. The license plate location algorithm applies edge detection and morphological algorithm. The principle of Radon transform is adopting binarization processing for the selected global threshold and morphological processing for the license plate to segment the connected region, on the basis of the vertical projection of local areas and the character segmentation of connected areas. At the end of this paper, a character recognition system based on improved BP neural network is taken. It can overcome some difficulties that traditional algorithm confronts, such as slow in getting the optimal solution and easy to fall into the local minimum. Also, compared with existing methods, this method has a fast convergence rate and recognition speed and a high accuracy.

An algorithm combined with color differential models for license-plate location

Vehicle license plate positioning is the first and most important step in license plate recognition systems. Existing license plate location algorithms are sensitive to light conditions and prone to be influenced by the background interference. To solve these problems, this paper presents an adaboost algorithm combined with color differential model. The proposed algorithm is composed of a coarse location step and a precise location step. In the coarse location step, a binary image is obtained to select the candidate plate regions using the color differential model. Then in the precise location step, the features obtained above together with other features are used by the adaboost algorithm to train the classifiers and precisely locate the license plates. The experimental results show that the proposed algorithm is more robust to the light conditions and background interference. In particular, during nighttime the precision rate can attain above 95.0%.

Multi-scaled license plate detection based on the label-moveable maximal MSER clique

In this paper, we consider a robust vehicle license plate detection problem for intelligent transportation systems in the presence of various illumination situations. We propose a robust and fast multi-scaled license plate detection and location algorithm, which exploits a Label-Moveable Maximal MSER clique. Specifically, first, we extract the candidate character regions using the Maximally Stable Extremal Region (MSER) features. Second, we divide each candidate character region into four types and extract the suspected initial node (the top-left character) based on its neighbor MSER distribution characteristic. Third, we label each candidate character region to accomplish license detection and location based on the detected suspected initial node and the corresponding label-moveable maximal MSER clique. The robust of license plate detection, the accuracy of character labeling for license location, and the improvement of calculation efficiency are evaluated via the real data.

Method of License Plate Location Based on Mathematical Morphology and Color Characteristics

According to restrictions existing in the application of single license plate location algorithm in complicated background. A license plate location algorithm based on mathematical morphology and color characteristics is proposed. Firstly, the new method uses Ostu algorithm to select an optimal threshold, and according to the threshold carries on binary processing, then by using a new method of mathematical morphology for image edge detection. Finally combined mathematical morphology with color characteristic to locate license plate .Experiments show that this new method with higher accuracy and fewer restrictions on background is superior to traditional or a single method and can be widely used.

一种改进的RGB Hough车牌校正定位算法

License plate localization plays a decisive role in license plate recognition.In order to improve the accuracy of character recognition by de-skewing,a license plate correction and location algorithm based on the RGB color space and Hough transform is proposed for the license plate of the blue background with white characters.It can locate the license plate after de-skewing for the license plate is inclined.For the license plate is not inclined,it can be located directly.The license plate localization accuracy is 93%for the license plate is not inclined,and it's 88%for the license plate is inclined.

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.

Self-adaptive model of texture-based target location for intelligent transportation system applications

In this paper, three shortages: full-enhancement mechanism, invariable texture extraction mechanism, and lack of robust enhancement methods, that are always neglected in some tasks based on texture-based target location for Intelligent Transportation System applications (ITS) are introduced. To cover these shortages, a self-adaptive model for texture-based target location is established. It is mainly composed of self-adaptive enhancement mechanism, self-adaptive texture extraction mechanism, and graying enhancement. Also, feedback evaluation strategy for design of texture extraction modes, needed by self-adaptive texture extraction mechanism, is provided. And then, an algorithm of license plate location is designed to evaluate effectiveness of this model. 338 license plate images from natural scenes were applied to test graying enhancement and self-adaptive location. Results of enhancement experiment indicate that graying enhancement is robust in purposiveness, real-time performance, and empirical parameter independence, simultaneously. Further, Results of location experiment demonstrate that not only a sound algorithm of license plate location with high success rate (96.1%) and low average execution time (52ms) is given but also the effectiveness of the two self-adaptive mechanisms in the proposed model are validated.

License plate location algorithm based on three-valued image

License plate location algorithm based on three-valued image

An Improved Method for Vietnam License Plate Location based on Mathematic Morphology and Measuring Properties of Image Regions

In the Automatic License Plate Recognition (ALPR) system, the License Plate Location (LPL) is the key step before the final recognition. This paper proposed an improved LPL algorithm for Vietnam vehicle license plates (LP). The proposed algorithm consists of three main modules: Pre-processing (convert RGB image to grayscale image, adjust grayscale image intensity, image binarization use Otsu method), LP candidates location (morphology opening to remove noises & dilation operation, measure properties of image regions to find candidates), LP exactly location (finding the LP angle & rotating LP, cut exactly LP region). We implemented test for 350 Vietnam vehicle images, which obtained from the actual system, the average rate of accuracy of our method is 98.64%, our results are more exactly presented methods.

License Plate Location Algorithm Based on Twin SVM

A method for license plate location based on Twin SVM is proposed in this paper. Firstly, the mathematical models of Twin SVM are introduced, and then the feature extractions of license plate image are discussed, finally it is proved that this location method is very precise and efficient by experiments in VC development environment.

The Research of Automatic License Plate Location Algorithm Under Various Conditions

The paper presents a method of automated license plate location, the first key step of the license plate recognition is to find and separate the license plate area from the image of license plate. In this article, the quality of the license plate image is improved though a series of digital image processing in the image pretreatment, and the quick and exact license plate location is realized based on the gray projection algorithm. Large numerous of plate license images are acquired and tested by the development platform of VC++6.0, and the result shows that the technology adopted in the article has good adaptability, especially it can quickly and reliably locate the license plate images shot under complex backgrounds.

Design and Implementation of the Automatic License Plate Location Algorithm under Complex Backgrounds

The automatic license plate recognition system is the important part of the modern intelligent transportation system (ITS), and the first key step of the license plate recognition is to find and separate the license plate area from the image of license plate. In this article, the quality of the license plate image is improved though a series of digital image processing in the image pretreatment, and the quick and exact license plate location is realized based on the gray projection algorithm. Large numerous of plate license images are acquired and tested by the development platform of VC++6.0, and the result shows that the technology adopted in the article has good adaptability, especially it can quickly and reliably locate the license plate images shot under complex backgrounds.