Problem Statement
Moving Vehicle Number Plate Detection
The process of identifying automobiles by their licence plates, known as licence plate recognition or LPR, combines image processing, character segmentation, and recognition technology. This method does not call for the installation of extra hardware on automobiles because it just uses the licence plate information for identification. Particularly in security and traffic control systems, LPR technology is steadily rising in prominence.
PS Number: PSdat005
Domain Bucket: Data Analytics
Category: Hardware
Dataset : NA
The phase of a typical system that takes the longest is licence plate localization and extraction. For LPR systems to be able to locate licence plates in real time, assumptions and optimizations are needed. However, concurrent to this growth in computing needs. Constraints and previous information are used to reduce this adverse effect.
Background of the Problem
License Plate Recognition (LPR) is a combination of image processing, character
segmentation and recognition technologies used to identify vehicles by their license
plates. Since only the license plate information is used for identification, this
technology requires no additional hardware to be installed on vehicles. LPR
technology is constantly gaining popularity, especially in security and traffic control
systems. License Plate Recognition Systems are utilized frequently for access control
in buildings and parking areas, law enforcement, stolen car detection, traffic control,
automatic toll collection and marketing research.
Objective
License plate localization and extraction are the most time consuming stage of a
typical system. Assumptions as well as optimizations are required in order for LPR
systems to be able to locate license plates in real time. However, the computational
requirements increase in parallel. To minimize this side-effect, constraints and prior
knowledge are utilized. After extracting the license plate area, the resulting region is
further processed for character segmentation and recognition.
Summary
The Segmented characters are identified by using Template Matching Method. The suggested method is tested with various types of vehicles like four wheelers and with yellow and white background. The number plates with additional unnecessary data are also segmented with great accuracy.