The auto license plate recognition technology is a pattern recognition technology that uses the dynamic video or static image of the vehicle to automatically identify the license plate number and license plate color. Through the collection and processing of images, the automatic license plate recognition function is completed. The license plate image can be automatically extracted from an image, and the characters are automatically segmented to further identify the characters. The hardware basis generally includes the trigger device (monitoring whether the vehicle enters the field of view), Camera cores, lighting equipment, image acquisition equipment, and processor (such as computers) that identify license plate numbers include the license plate location algorithm, license plate character segmentation algorithm, and optical character recognition algorithm. Certain license plate recognition systems also have the function of judging the vehicle's visibility into the field of view through video images, referred to as video vehicle detection. A complete license plate recognition system should include several parts such as vehicle detection, image acquisition and license plate recognition (as shown in Figure 1). When the vehicle detection section detects the arrival of the vehicle, the image acquisition unit is triggered to collect the current video image. The license plate recognition unit processes the image, locates the license plate position, and then separates the characters in the license plate for identification, and then composes the license plate number.
Vehicle detection
Vehicle detection can use buried coil detection, infrared detection, radar detection, video detection and other methods. The use of video detection can avoid damage to the road surface, eliminate the need for additional external inspection equipment, eliminate the need to correct the trigger position, save costs, and is more suitable for mobile and portable applications.
A license plate recognition system with a video-vehicle detection function first acquires and digitizes a frame (field) signal in a video signal to obtain a corresponding digital image; and then analyzes it to determine whether there is a vehicle; if there is If the vehicle passes, it will enter the next step for license plate recognition; otherwise it will continue to collect video signals for processing.
The system performs video vehicle detection, requires high processing speed and adopts an excellent algorithm to achieve image acquisition and processing without dropping frames. If the processing speed is slow, it will result in dropped frames, making it impossible for the system to correctly detect vehicles with fast traveling speeds, and it is also difficult to ensure that recognition processing is started at a location that is conducive to recognition, and the system recognition rate is affected. Therefore, combining video vehicle detection with automatic license plate recognition has certain technical difficulties.
2. License number, color identification
In order to carry out license plate recognition, the following basic steps are required:
• License plate location, positioning of the license plate in the picture;
• Splitting the license plate character to separate the characters in the license plate;
• License plate character recognition, which identifies the segmented characters and eventually composes the license plate number.
In the license plate recognition process, the identification basis of the license plate color is different, which may be achieved 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, and several regions that meet the characteristics of the license plate are found as candidate regions. Then these candidate regions are further analyzed and judged, and finally an optimal region is selected as the license 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 Plate Character Recognition
The character recognition method is mainly based on template matching algorithm and artificial neural network algorithm. Based on the template matching algorithm, the divided characters are first binarized, and their size is scaled to the size of the template in the character database. Then the template is matched with all the templates, and the best match is selected as the result. There are two kinds of algorithms based on artificial neural network: one is to first treat the recognition characters for feature extraction, and then use the obtained features to train the neural network distributor; the other method is to directly input the image to be processed into the network, and the network automatically Feature extraction is performed until the result is identified.
In practical applications, the recognition rate of the license plate recognition system is 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, stains, peeling paint, faded fonts, blocked license plates, tilted license plates, bright reflective, multiple license plates, fake license plates, etc. The actual shooting process will also be subject to ambient brightness. , shooting brightness, vehicle speed and other factors. These influencing factors reduce the recognition rate of license plate recognition to different extents. It is also the difficulty and challenge of the license plate recognition system. In order to improve the recognition rate, in addition to constantly improve the recognition algorithm, we should also think of ways to overcome various lighting conditions, so that the captured image is most conducive to identification.
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