Vertical-edge-based car-license-plate detection method

This paper proposes a fast method for car-license-plate detection (CLPD) and presents three main contributions. The first contribution is that we propose a fast vertical edge detection algorithm (VEDA) based on the contrast between the grayscale values, which enhances the speed of the CLPD method. After binarizing the input image using adaptive thresholding (AT), an unwanted-line elimination algorithm (ULEA) is proposed to enhance the image, and then, the VEDA is applied. The second contribution is that our proposed CLPD method processes very-low-resolution images taken by a web camera. After the vertical edges have been detected by the VEDA, the desired plate details based on color information are highlighted. Then, the candidate region based on statistical and logical operations will be extracted. Finally, an LP is detected. The third contribution is that we compare the VEDA to the Sobel operator in terms of accuracy, algorithm complexity, and processing time. The results show accurate edge detection performance and faster processing than Sobel by five to nine times. In terms of complexity, a big-O-notation module is used and the following result is obtained: The VEDA has less complexity by K2 times, whereas K2 represents the mask size of Sobel. Results show that the computation time of the CLPD method is 47.7 ms, which meets the real-time requirements

A Fast Vertical Edge Detection Algorithm for Car License Plate Detection

Recently, License Plate Detection (LPD) has been used in many applications especially in transportation systems. Many methods have been proposed in order to detect license plates, but most of them worked under restricted conditions, such as fixed illumination, stationary background, and high resolution images. LPD plays an important role in Car License Plate Recognition (CLPR) system because it affects the system's accuracy. This thesis aims to propose a fast vertical edge detector using Vertical Edge Detection Algorithm (VEDA) and to build a Car License Plate Detection (CLPD) method. Pre-processing step is performed in order to enhance and initialize the input image for the next steps. This step is divided into three processes: First, the color image conversion to a gray scale image. Second, an adaptive thresholding is used in order to constitute a binarized image. Third, Unwanted Lines Elimination Algorithm (ULEA) is used in order to enhance the image. The next step is to extract the vertical edges from the 352x288 resolution image by using VEDA. This algorithm is based on the contrast between the values in the binarized image. VEDA is proposed in order to enhance the CLPD method computation time. After the vertical edges have been extracted by VEDA, a morphological operation is used to highlight the vertical details in the image. Next, candidate regions are extracted. Finally, the license plate area is detected. This thesis shows that VEDA is faster than Sobel operator; the results reveal that VEDA is faster than Sobel by about 5-9 times, this range depends on the image resolution. The proposed CLPD method can efficiently detect the license plate area. The method shows the total time of processing one 352x288 image is 47.7 ms, and it meets the requirement of real time processing. Under the experiment datasets, which were taken from real scenes, 579 from 643 images are successfully detected. The average accuracy of car license plate detection is 90%. For more evaluation and comparison purposes, the proposed CLPD method is compared with a similar Malaysian license plate detection method, which is CAR Plate Extraction Technology (CARPET). This comparison reveled that the CLPD method is more efficient than CARPET and also has more detection rate

Efficiently locating vehicle license plates based on vertical line detection

[[abstract]]A fast method for locating vehicle license plates is proposed in this paper. This approach is primarily based on the observations that the intensity contrast between license plate background color and symbol color is high and that symbol edges are clustered in the plate area. A simple edge detection method based on first derivatives is applied to a car image such that the binary image containing only vertical edges is obtained. Then possible plate regions could be located from the horizontal and vertical projections of the binary image. No preprocessing, such as image enhancement and noise filtering, is required. In addition to its simplicity and fastness, the proposed method is less sensitive to illumination changes of the plate. The proposed method was experimented with 117 diverse car images on a Pentium-133 processor. The system successfully located 116 license plates in 0.2 second on an average.[[notice]]補正完畢[[conferencetype]]國內[[conferencedate]]19990822~19990824[[conferencelocation]]臺北縣, 臺

Empirical Study of Car License Plates Recognition

The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison

License plate localization based on statistical measures of license plate features

— License plate localization is considered as the most important part of license plate recognition system. The high accuracy rate of license plate recognition is depended on the ability of license plate detection. This paper presents a novel method for license plate localization bases on license plate features. This proposed method consists of two main processes. First, candidate regions extraction step, Sobel operator is applied to obtain vertical edges and then potential candidate regions are extracted by deploying mathematical morphology operations [5]. Last, license plate verification step, this step employs the standard deviation of license plate features to confirm license plate position. The experimental results show that the proposed method can achieve high quality license plate localization results with high accuracy rate of 98.26 %

Car license plate detection method for Malaysian plates-styles by using a web camera

Recently, license plate detection has been used in many applications especially in transportation systems. Many methods have been proposed in order to detect license plates, but most of them work under restricted conditions such as fixed illumination, stationary background, and high resolution images. License plate detection plays an important role in car license plate recognition systems because it affects the accuracy and processing time of the system. This work aims to build a Car License Plate Detection (CLPD) system at a lower cost of its hardware devices and with less complexity of algorithms' design, and then compare its performance with the local CAR Plate Extraction Technology (CARPET). As Malaysian plates have special design and they differ from other international plates, this work tries to compare two likely-design methods. The images are taken using a web camera for both the systems. One of the most important contributions in this paper is that the proposed CLPD method uses Vertical Edge Detection Algorithm (VEDA) to extract the vertical edges of plates. The proposed CLPD method can work to detect the region of car license plates. The method shows the total time of processing one 352x288 image is 47.7 ms, and it meets the requirement of real time processing. Under the experiment datasets, which were taken from real scenes, 579 out of 643 images were successfully detected. Meanwhile, the average accuracy of locating car license plate was 90%. In this work, a comparison between CARPET and the proposed CLPD method for the same tested images was done in terms of detection rate and efficiency. The results indicated that the detection rate was 92% and 84% for the CLPD method and CARPET, respectively. The results also showed that the CLPD method could work using dark images to detect license plates, whereas CARPET had failed to do so

Training-Free License Plate Detection Using Vehicle Symmetry and Simple Features

In this paper, we propose a training free license plate detection method. We use a challenging benchmark dataset for license plate detection. Unlike many existing approaches, the proposed approach is a training free method, which does not require supervised training procedure and yet can achieve a reasonably good performance. Our motivation comes from the fact that, although license plates are largely variant in color, size, aspect ratio, illumination condition and so on, the rear view of vehicles is mostly symmetric with regard to the vehicles central axis. In addition, license plates for most vehicles are usually located on or close to the vertical axis of the vehicle body along which the vehicle is nearly symmetric. Taking advantage of such prior knowledge, the license plate detection problem is made simpler compared to the conventional scanning window approach which not only requires a large number of scanning window locations, but also requires different parameter settings such as scanning window sizes, aspect ratios and so on

Fuzzy and Neural Network Based License- Plate Localization and Recognition

ABSTRACT: This paper presents the extraction of vehicle license plate information from a sequence of various images. ALPR is used in the presence or absence of a database in various applications such as, toll payment, etc. The proposed technique implements the CAN image by using a camera and to extract the license plate from the image based on various characteristics, such as the boundary, the color, or the existence of the characters. Thus it is not suitable for location of variable number plate. Finally to recognize the extracted characters by template matching by using neural networks and fuzzy classifiers. KEYWORDS: vertical edge detection algorithm (VEDA), scale-invariant feature transform(SIFT), dynamic programming(DP). Observing the notion of cars and do not belong in a parking garage. I.INTRODUCTION Automated By eliminating the parking as expedited which is the need for human confirmation of parking passes. The organization of the paper is mentioned as follows. In section II, a detailed review of ALPR Technique. Section III , illustrates the various algorithms used in ALPR Technique. Section IV features out the proposed method. Section V and VI narrates the experimental and simulation results and Section VII concludes the paper and defines the future works. ISSN (Print Vol. 3, Issue 3, March 2014 Copyright to IJAREEIE www.ijareeie.com 8144 II. ALPR TECHNIQUE ALPR is known by several other names, including Automatic Number Plate Recognition (ANPR), Automatic Vehicle Identification (AVI), Car Plate Recognition (CPR), License Plate Recognition (LPR), and Lecture Automatique de Plaquesd' Immatriculation (LAPI). Other name of ALPR are followed as car plate recognition, automatic vehicle identification, and optical character recognition for cars Optical character recognition, usually abbreviated as OCR, is conversion by electronic and mechanical of scanned images which may be of writing by hand, or printed characters and numbers into machine-encoded format of characters and numbers. It is widely used as a form of data entry from some sort of original paper data source, whether documents, sales receipts, mail, or any number of printed records It is a common method of digitizing printed characters and numbers so that they can be electronically searched, stored more compactly, displayed on-line, and used in machine processes such as machine translation, textto-speech and text mining[6]- Early versions needed to be programmed with images of each and every texts at a time implied on a single font. "Intelligent" systems with a greater degree of recognition accuracy for most fonts are now common III. ALGORITHMS USED IN ALPR TECHNIQUE The ALPR system comprising of four levels from the input image results during the extraction of a license plate number. The first level is the location of an image of a car by utilizing a camera. Vol. 3, Issue 3, March 2014 Copyright to IJAREEIE www.ijareeie.com 8145 The second level is the extraction of a license plate from the given input image based on the following features as boundary, existence of the characters and the color. The third level is the character extraction and segmentation of license plate. The final level is the recognization of character extraction by the matching of templates such as fuzzy classifiers. This paper illustrates the procedural types of various algorithms, They are as follows: A. Block converter Block converter is the converter ,which is used to convert the image to sub blocks from the sub block , use selected box only for segmenting the number plate. The block-based method is also presented in which the blocks with greater magnitude edges are visualized as areas of license plate. Vol. 3, Issue 3, March 2014 Copyright to IJAREEIE www.ijareeie.com 8146 B. Otsu The number plate consist of fore ground information and back ground data. In this algorithm the necessity of numbers only utilize the threshold segmentation. C. State-of-Art It is the feature based process. Training data convert to feature points. Feature point only depends upon the shape of the training image. Then it is compared to the number plate feature points Backgrounds of license plate and characters has varying colors, possessing opposite binary values in the binary image. Horizontal projection of used to extract the characters along with noise removal and analyzing the simplicity. To convert grayscale im-age into a binary image by using a threshold operation. There are basically two types of threshold operation