yudberi harriett keylee
Berniece Leonhardt
First, install MATLAB version 2014a or later on your system. Store the vehicle_number_plate.m MATLAB source code file in a folder. Make sure that the vehicle number plate image file is present in the source code folder.
The program will output the image, as shown in Fig. 1. If the output vehicle number plate is not properly displayed on the screen, you can change the parameters of the input arguments BW3=medfilt2(BW3,[ ]) in the code, as shown in Fig. 2.
The main objective of this project is to make an effective automatic number plate detection system. In this modern era with the increase in population the number of vehicles have also drastically increased. As the population is increasing the number of vehicles are also drastically increasing, so identifying and maintaining the record of vehicle numbers becomes difficult. There should be an automatic fraud detection system to know whether the vehicle number is valid or not. In some cases there might be two vehicles with the same number plate and manually recording and checking for these vehicles is time taking, not efficient and requires manpower. To deal with this issue an automatic number plate detection system is built where it will take the image of the vehicle, crop the number plate and process it and tell whether the number is valid or not. It helps us to eliminate the problems in identifying vehicles for traffic control and it can be utilised in various places like hospitals, shopping malls, public places etc. In matlab the automatic number plate detection system(ANPD) is built with the help of image processing and bounding box technique. This system also takes the method of OCR to detect the number plate.
Note: if we want to add more images, extra images should be stored in the Number plate images folder in the zip file. The additional images should have the same width and length and quality as previous images.
I have a long string like "sdnak hsd fds fnsdf APsdf09sdf BN fddsdalf 7886sd f" from this string I have to extract "AP09BN7886" which is actually a vehicle's licence plate number in INDIA. I know possibly the easiest is to use regular expression, can anybody tell me the reg. exp to find this.
You are trying to gather components of that from a long string, and you have not stated enough detail of the other parts of the string to completely eliminate ambiguity (for example: The letters are all lower case or non latin characters? Only one license plate per string? Only two letters prefixing the four digits? etc) Since this is not known, you can represent the 'sea' of characters between match groups with .*?
Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image. The system model uses already captured images for this recognition process. First the recognition system starts with character identification based on number plate extraction, Splitting characters and template matching. ALPR as a real life application has to quickly and successfully process license plates under different environmental conditions, such as day time. It plays an important role in numerous real-life applications, such as automatic toll collection, traffic law enforcement, parking lot access control, and road traffic monitoring. The system uses different templates for identifying the characters from input image. After character recognition, an identified group of characters will be compared with database number plates for authentication. The proposed model has low complexity and less time consuming in terms of number plate segmentation and character recognition. This can improve the system performance and make the system more efficient by taking relevant samples. at the same time compared their advantages and disadvantages, which provide the basis for license plate recognition.
An efficient less time consuming vehicle number plate detection method is projected which performed on multifaceted image. By using, Sobel edge detection method here detects edges and fills the holes less than 8 pixels only. To removing the license plate we remove connected components less than 1000 pixels. Our anticipated algorithm is mainly based on Indian automobile number plate system. Extraction of number plate accuracy may be increased for low ambient light image.
This document summarizes a vehicle number plate recognition system using MATLAB. It contains the following sections: contents, block diagram of the system, characters recognition, characters segmentation, character recognition, applications, and conclusions. The system works by acquiring an image of a license plate, processing it, segmenting the characters, recognizing each character, and validating the registration. Character recognition is done using artificial neural networks trained on letters and numbers. Applications include traffic signals, border crossings, and recognizing customers based on license plates. The conclusion is that the system can detect license plates easily and reduce processing time reliably.Read less
Abstract:- Automatic vehicle number plate detection is the technique of extracting an area of license number plate from captured vehicle image. The development of number plate detection system includes image capturing, Pre-processing where a lot of disturbances and noises present in the image cleared and Plate region extraction which is the process of extracting license number plate area. This research, focus on introduction of Ethiopian number plate unique feature, selection of appropriate algorithm and technology required for detection system development finally, by taking sample input image of vehicle a system with an in-house built MATLAB code successfully developed for the preprocessing and detection of Ethiopian vehicles number plate area.
several concepts of image processing, such as gray scale conversion, dilation, noise filteringedge processing, detection of probable areas and others. [1]Figure 1 shows an example of Ethiopian license plate which contains Numeric and Amharic character codes printed in rectangular plates .Ethiopia has unique license plate styles which reflects three Categories of information such as Location of Vehicle Number plates issued region, The intended service of the car, Particular identification number for the car with a different color background [2] these features are shown in fig 1, Table 1 and Table 2 respectively
Yasser M.Alginahi presents recognition of Arabic license plate system by using a neural network, Horizontal projection, and zoning, 470 image samples are used for feature extraction with a precession of approximately 97%. [3].M.M Shidore and s.p. Narote discuss recognition of Indian vehicle number by using different algorithm SVM, Vertical edge detection and connected component analysis with a precession of approximately 78.84% [4].Deepti sagar and maitreyee dutta proposed vehicle number plate recognition in case of India by using block based neural network with a precession of
approximately 98.2%. [5] Seble Nigussie and Yaregal Assabie presents Ethiopian License Plates Recognition by using Correlation based template matching and use Gabor filter for plate detection and CCA for feature extraction get an accuracy approximately 71.06%.[6]. M. K. B. Ashan1 and N. G. J. Dias studied recognition of vehicle license plates in sri lank using matlab Foreground detection and blob analysis used for vehicle detection, edge processing and filtering for license plate isolation and finally the OCR from the detected license plate by using character segmentation and template matching mehods and get accuracy around 85 %for detection of license plate and 70 % for recognition of license plate.[7] Ibrahim turkylmal and Kirami Kacan present recognition of vehicle number plate system by using Artificial Neural Networks, divide the recognition process into three stages, license plate area detection using, segmentation of character on the number plate, and finally recognition of character on the number plate. for license plate area detection Edge-based image processing Techniques is used, for segmentation of character vertical projections in Binarized images for area used and to recognize character on number plate ANN is used got success rate around 97%.[8]Reshu kumara and surya Prakash Sharma presents automatic number plate recognition by using machine learning techniques and divided the process into three capturing the image, plate localization and recognition of digits on the plate and also use HOG features
for training and SVM for classification, they obtained is 99%accuracy.[9] .P.surekha et.al presents vehicle number plate recognition by using image processing and neural network and obtained 97% accuracy.[10].fei Xie el.al proposed license plate detection and character recognition system based on a combined feature extraction model and also use BPNN, for feature combination, training the feature vectors to get the best accuracy of 97.7%.[11] Inga astawa et.al presents Detection of vehicle number Plate by using Sliding Window, Histogram of Oriented Gradient, and Support Vector Machines also use mobile phone to identify location of vehicle number plate get recognition accuracy of 96%.[12] Gajendra Sharma presents Analysis of Vehicle Number Plate Recognition System and their performance by Using Template Matching Techniques also connected component analysis used for feature extraction on 90 sample images get 67.98% cross correlation and 63.46% phase correlation. [13].
The main motivation of this project work is to create Ethiopian license plate detection system framework that requires less human involvement for the traffic control process and increased manageability of the traffic system.
The first step is to preprocess the imported in Red Green Blue (RGB) format Followed by the conversion of the imported RGB image to grayscale to reduce the number of colors, which reduces the brightness. Which to some extent helps to reduce the noise in the image and facilitates the processing of the image. Figure 3 shows a gray transformed image of a vehicle with a gray filter applied
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