Pic 1.3.1 - Named after muhammad al-khorezmi tashkent university of information technologies fergana branch

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Named after muhammad al-khorezmi tashkent university of information technologies fergana branchBog'liq m12Pic 1.3.1 Typical ANPR System Diagram of a Fixed ANPR System (right) and a Mobile ANPR System (left).
Number Plate Recognition involves acquisition of number plate images from the intended scene, using a camera. Either still images or a photographic video is captured and further processed by a series of image processing based recognition algorithms to attain an alpha-numeric conversion of the captured images into a text entry. After obtaining a good quality image of the scene/vehicle, then the core dependence of any ANPR system is on the robustness of its algorithms. These algorithms need a very careful consideration and require thousands of lines of software coding to get desired results and cover all system complexities. As a whole, a series of primary algorithms are necessary for smart vehicle technologies and ANPR to be effective. The general processes involved in ANPR systems is shown in Pic 1.3.2.
Pic 1.3.2 General processes of number plate recognition system.
A typical ANPR system goes through the general process of image acquisition (input to the system), number plate extraction (NPE), character segmentation (CS) and character recognition (CR) (as output from the system) [9]. After successful recognition of the vehicle the data can be accessed and used for post processing operations as required. The vehicles data is sent to the connected back office system software which is the central repository to all data along with tools to support data analysis, queries and reporting accordingly. This data collected can be utilized for several other intelligent transportation applications since ANPR systems not just visually capture the vehicle images but also record the metadata in their central repository. This can potentially include vehicle recognition through date and time stamping as well as exact location, whilst storing a comprehensive database of traffic movement. This data can be helpful in modelling different transport systems and their analysis.
The image taken from the scene may experience some complexities depending upon the type of camera used, its resolution, lightening/illumination aids, the mounting position, area/lanes coverage capability, complex scenes, shutter speed and other environmental and system constraints. Pic 1.3.2 shows License plate diversity in styles, colors, fonts, sizes, and physical conditions; which may affect the recognition accuracy. When a vehicle is detected in the scene/image, the system uses plate localization functions to extract the license plate from the vehicle image, a process commonly termed as Number Plate Extraction. Characters on the extracted number plate are then segmented prior to recognition process. Character segmentation is an algorithm that locates the alpha numeric characters on a number plate. The segmented characters are then translated into an alpha numeric text entry using the optical character recognition (OCR) techniques. For character recognition, algorithms such as template matching or neural network classifiers are used. The performance of an ANPR system relies on the effectiveness of each individual stage. A parameter used to quantify the whole process is the performance-rate or success-rate, which is the ratio of the number of number-plates successfully recognized to the total number of input images taken. The performance rate involves all the three stages of recognition process, number plate extraction, segmentation and character recognition.
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