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Automatic optical character recognition (ALPR) is the extraction of vehicle optical character 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. 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 optical character recognition. Basically, for the identification of the optical character take character reorganization. And before than that localize the area of the optical character. By using this process we can identify the number plate of the vehicle. These are used for the identification of the vehicle when we lost our vehicle.
Keywords: Character recognition, Region of interest (ROI),Morphological filters
The tasks of managing and using cars well, cracking theft and robbery of motor vehicles, as well as maintaining the normal order of urban transport have become increasingly heavy. Currently, it has become an important issue for the public security department to tom static management into dynamic change management and to tumor manual management into automation. There are urgent needs to employ Intelligent Transportation System (ITS) so as to make effective management. ITS can perform efficient and reliable management to ambient vehicles under various circumstances. As one of the core technologies of ITS, Vehicle Feature Recognition Technology is an important link to police enforcement system, automated highway toll collection system, Urban Traffic Surveillance System and Intelligent Parking Management System, etc. Thus employing image processing technology to recognize the vehicle optical character number of various kinds of vehicles is not only an important issue for information process technology, but also a research issue which is of great importance in modem transportation management.
For the detection of license number plate detection using character reorganization. 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. Using this process we can identify the authentication person. And we can track the vehicle also. Using this we can find out our vehicle number plate and get our vehicle. For that purpose we are using morphological filters for adding colour. Take character reorganization and segmentation. Then we can get the output.
Local Binary Pattern?(LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. Due to its discriminative power and computational simplicity, LBP texture operator has become a popular approach in various applications. It can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Perhaps the most important property of the LBP operator in real-world applications is its robustness to monotonic gray-scale changes caused, for example, by illumination variations. Another important property is its computational simplicity, which makes it possible to analyze images in challenging real-time settings.
The basic idea for developing the LBP operator was that two-dimensional surface textures can be described by two complementary measures: local spatial patterns and gray scale contrast. The original LBP operator (Ojala et al. 1996) forms labels for the image pixels by thresholding the 3 x 3 neighborhood of each pixel with the center value and considering the result as a binary number. The histogram of these 28?= 256 different labels can then be used as a texture descriptor. This operator used jointly with a simple local contrast measure provided very good performance in unsupervised texture segmentation (Ojala and Pietik?inen 1999). After this, many related approaches have been developed for texture and color texture segmentation.
The LBP operator was extended to use neighborhoods of different sizes (Ojala et al. 2002). Using a circular neighborhood and bilinearly interpolating values at non-integer pixel coordinates allow any radius and number of pixels in the neighborhood. The gray scale variance of the local neighborhood can be used as the complementary contrast measure. In the following, the notation (P,R) will be used for pixel neighborhoods which means P sampling points on a circle of radius of R.? for an example of LBP computation.
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
The proposed model has low complexity and less time consuming in terms of? optical character segmentation and character recognition. This can improve the system performance and make the system more efficient by taking relevant samples
Four algorithms of image preprocessing, optical character location, optical character segmentation and character recognition are introduced in this paper. Optical character location is the basis of image preprocessing. The location of optical character has a direct impact on the accuracy of character segmentation. In our project vehicle plate detection is obtained by using character recognizatio.and region of interest.
According to the licence plate decetion feature extraction process is important.during this detection we can detect the thief when he will taken the vehicle.here we are using morphological filters for the addition of color to detect the features.maximum we are using on roads and car parking areas.for the future purpose we can use deep neural network algorithm.using this process we can detect more vehicles at a time.these can be use at traffic,parking ares,airport etc..for the future purpose we can avoid roberries.
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