Abstract

Automatic license Plate Recognition (ALPR) is the method of extracting vehicle license plate information from an image or a sequence of images. A design of a new Genetic Algorithm (GA) is introduced to detect the locations of the License Plate (LP) symbols. The extracted information can be used with or without a database in number of application applications, such as electronic payment systems, toll payment, parking fee payment and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the Automatic License Plate Recognition. Connected Component Analysis Technique (CCAT) is used to detect candidate objects inside the unknown image. A scale-invariant Geometric Relationship Matrix (GRM) has been introduced to model the symbols layout in any LP which simplifies system adaptability. Most of CCAT problems such as touching or broken bodies have been minimized by modifying the GA .The system as a real- life application has to quickly and successfully process the license plates. These plates usually contain different colors, and different fonts. Some plates may have a single color background and some others have background images. The license plates can be partially occluded by dirt, lighting, and other accessories on the car. The system recognizes the plate by appropriate detection.