Abstract
Vehicle License Plate Detection (LPD) is an important step for the vehicle plate recognition which can be used in the intelligent transport systems. Many methods have been proposed for the detection of license plates based on: Mathematical morphology, Discrete Wavelet Transform, Hough Transform and others. In general, an LPR system includes four main parts: Vehicle image acquisition, license plate detection, character segmentation and character recognition. In this study, we present a robust method for extracting and detecting license plates, from simple images of Tunisian vehicles, based on Gabor filters and neural networks. The proposed method is designed to perform recognition of any kind of license plates under any environmental conditions.
Highlights
An License-Plate Rectangle (LPR) system includes four main parts which are represented as Fig. 1.To track and analyze vehicles motion, severalThe LP detection step enables the classification of intelligent transport systems have been developed to the non LP and the LP regions
Vehicle License Plate Detection (LPD) is an important step for the vehicle plate recognition which can be used in the intelligent transport systems
We present a robust method for extracting and detecting license plates, from simple images of Tunisian vehicles, based on Gabor filters and neural networks
Summary
The LP detection step enables the classification of intelligent transport systems have been developed to the non LP and the LP regions. This step is considered simplify the problem of identification of vehicles as the most important one, insofar as a good through various techniques which usually support on localization leads to a high accuracy and a real time automated algorithms. The license plate can be segmentation and recognition. The need for each character image block is kept for recognition. The character recognition step converts images automatic license plate recognition is due to several based on predefined recognition models
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
