Abstract
In today's world, withincreasing population number of vehiclesare also increased. Monitoring each andevery vehicle in roadside by manualforce becomes a tedious job. For effective traffic management some solution is required. The traditional system of licenseplate recognition basically relies on themorphological processing of images. The accuracy of recognition is also lower. Inorder to provide an efficient solution forlicense plate position, we propose a new method of license plate recognition. In the license plate position, we use traditional positioning method and Haar Cascade algorithm to detect the licenseplate. In addition with the traditionalidentification system cutting and matching, we use the ability feature extraction of Convolution Neural Network (CNN) to detect the license platedirectly, which avoids the recognitionerror which is caused by the segmentation in the license platerecognition. In this project, we propose a new license plate recognition system. It is divided into two parts, they are licenseplate positioning and characterrecognition.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.