#Car number plate recognition software download license
involved crash and e-bike license plate and to account for the correlations between them. A bivariate probit (BP) model was developed to simultaneously examine the significant factors associated with e-bike. Noncrash samples were collected by a questionnaire survey. E-bike crashes data were collected from police database and completed through a telephone interview. The primary objective of this study is to evaluate factors affecting e-bike involved crash and license plate use in China. For further research, it is necessary to introduce vehicle ownership and provide parking information so that the parking system becomes more perfect. Based on the experimental results, the system can recognize plate numbers with an accuracy of 83%.
The template matching image processing technique was chosen to solve this problem. The system created can also open and close the door latch automatically. To overcome this, the solution given in this study is to create a parking system that can read plate numbers automatically and store vehicle entry data directly into the database. Of course, this is not effective and efficient because the use of artificial intelligence is not purely done by the system.
Although the existing system already uses artificial intelligence to read plate numbers, the officers are still matching it. In addition, the existing system only discusses the opening and closing of bars and providing information on parking lots. Most of the existing parking systems still require parking attendants. With the increasing population of motorized vehicle users, it will result in an increase in problems that occur in traffic violations and also the technology security system in. 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.īased on data from the Central Statistics Agency in 2018, it was written that the number of motorbikes for the Indonesian region was 120.10 million or 82% and for cars 26.75 million or around 18% of the total population. The experimental results with different license plate demonstrate effectiveness and efficiency of the proposed algorithm under various complex backgrounds. 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. 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. Firstly, a preprocessing is first used to strengthen the contrast ratio of original car image. 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. The rapid development of the license plate recognition technology has made great progress for its widespread uses in intelligent transportation system (ITS).
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