AUTOMATIC NUMBER PLATE RECOGNITION
DESIGN DETAILS
In recent years, license plate recognition system has become a crucial role in the development of smart cities for vehicle management, investigation of stolen vehicles, and traffic monitoring and control. License plate recognition system has three stages, including license plate localization, character segmentation, and character recognition. Although the license plate recognition system has been successfully applied to the environment-controlled smart parking system, it still faces many challenging in the surveillance system such as congested traffic with multiple plates, ambiguous signs, and advertisements, tilting plates, as well as obscure images taken in bad weather and night-time.
The Detection stage of the license plate (LP) is the most critical step in an automatic vehicle identification system. Detecting license text and at the same time distinguishing it from similar patterns based on the geometrical relationship between the symbols constituting the license numbers is the selected approach. In this design first detects LP symbols without using any information associated with the plate’s outer shape or internal colors to allow for the detection of the license numbers in case of shape or color distortion either physically or due to capturing conditions, such as poor lighting, shadows, and camera position and orientation. This Matlab design is based on license plate recognition system using ANFIS and Genetic Algorithm that first detects vehicles and then retrieves license plates from vehicles to reduce false positives on plate detection.
REFERENCES
Reference Paper-1: A New Approach for Vehicle Number Plate Detection
Author’s Name: Sarthak Babbar, Saommya Kesarwani, Navroz Dewan and Kartik Shangle
Source: IEEE
Year: 2018
Reference Paper-2: Localization of License Plate Number Using Dynamic Image Processing Techniques and Genetic Algorithms
Author’s Name: G. Abo Samra and F. Khalefah
Source: IEEE
Year: 2014
Reference Paper-3: Automatic Licence Plate Detection Using Adaptive Neuro Fuzzy Inference System and Wavelet Packets.
Author’s Name: S. P. Jainaveen,M. Jaya Bharata Reddy and Dusmanta Kumar Mohanta
Source: Australian Journal of Multi-Disciplinary Engineering
Year: 2017
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