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License Plate Recognition Process
License plate recognition system (Vehicle License Plate Recognition, VLPR) is an application in a computer video image recognition technique of vehicle license plate recognition. License plate recognition is widely used in the management of highway vehicles. In the Electronic Toll Collection (ETC) system, it is also the main means to identify vehicle identities in conjunction with DSRC technology.
For license plate recognition , the following basic steps are required:
1) Positioning of the license plate and positioning of the license plate in the picture;
2) The license plate character is divided and the characters in the license plate are separated out;
3) Recognition of license plate characters, identifying the segmented characters and eventually composing the license plate number.
In the license plate recognition process, the identification color of the license plate is based on different algorithms, which may be realized in the above-mentioned different steps, and usually cooperate with the license plate recognition to verify each other.
1) License location
Under the natural environment, the background of the car image is complex and the illumination is uneven. How to accurately determine the license plate area in the natural background is the key to the entire recognition process. Firstly, a wide range of relevant search is performed on the collected video images to find a number of areas that meet the characteristics of the automobile license plate as candidate areas. Then these candidate areas are further analyzed and judged, and finally an optimal area is selected as the license plate area. Separate it from the image.
2) License plate character segmentation
After completing the positioning of the license plate area, the license plate area is divided into individual characters and then recognized. Character segmentation generally adopts vertical projection method. Since the projection of a character in the vertical direction necessarily takes place near the local minimum between characters or spaces within the character, this position should satisfy the character writing format, characters, size restrictions, and some other conditions of the license plate. Using vertical projection method has a good effect on character segmentation in car images under complex environments.
3) License character recognition methods mainly include template matching algorithms and artificial neural network algorithms. The template-based matching algorithm first binarizes the segmented characters and scales their size to the size of the template in the character database, then matches all the templates and selects the best match as the result. There are two kinds of algorithms based on the artificial neural network: one is to extract features from the characters first, and then use the obtained features to train the neural network distributor; the other method is to directly input the images into the network, and the features are automatically extracted by the network until Identify the result.
In practical applications, the recognition rate of the license plate recognition system is also closely related to the license plate quality and the shooting quality. The quality of the license plate can be affected by various factors such as rust, stain, paint peeling, font fade, license plate obstruction, license plate tilt, bright reflective, multiple license plates, fake license plates, etc.; the actual shooting process will also be subject to environmental brightness , shooting methods, vehicle speed, and other factors. These influencing factors reduce the recognition rate of license plate recognition in different degrees. It is also the difficulty and challenge of the license plate recognition system. In order to improve the recognition rate, besides constantly improving the recognition algorithm, it should also find a way to overcome various lighting conditions, so that the captured image is most conducive to recognition.