One-stage Vehicle Engine Number Recognition System

Author(s):  
Cheng-Hsiung Yang ◽  
Han-Shen Feng
2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091727
Author(s):  
Zeyou Chen ◽  
Yangyang Su ◽  
Yong Liu ◽  
Jiazhen Huang ◽  
Wuwen Cao

With the development of economy, the research of urban intelligent transportation system is becoming more and more important. The research and development of plate number recognition system is an important factor to realize the intelligence and modernization of transportation system. It uses each car to have a unique plate number and recognizes the vehicle number through the vehicle image captured by the camera. On the basis of image recognition, this article takes plate number image as the research object and discusses the key technologies of plate number recognition system. First, this article uses image preprocessing technology to process images to improve image quality. Second, the plate number location algorithm based on the connected region search is analyzed. According to the characteristics of the plate number itself, the regional features of the plate number are extracted to locate the plate number accurately. Then, an improved vertical projection-based plate number character segmentation method is proposed to segment plate number characters. Finally, combined with character characteristics, the template matching method is used to recognize plate number characters. The simulation results show that, on the basis of image recognition, this article studies the key technologies of plate number recognition system, which effectively improves the performance of the system and makes the recognition of plate number more effective and accurate.


Author(s):  
Shakeeb M.A.N. Abdul Samad ◽  
Fahri Heltha ◽  
M. Faliq

Car Plate Number Recognition System is an important platform that can be used to identify a car vehicle identity. The Recognition System is based on image processing techniques and computer vision. A webcam is used to capture an image of the car plate number from different distance, and the identification is conducted through  four processes of stages: Image Acquisition Pre-processing, Extraction, Segmentation, and Character Recognition. The Acquisition Pre-processing stage is extracted the region of interest of the image. The image is captured by live video of the webcam, then converted to grayscale and binary image. The Extraction stage is extracted the plate number characters from binary image using a connected components method. In the Segmentation stage is done by implementing horizontal projection as well as moving average filter. Lastly, in the Character Recognition, is used to identify the segmented characters of the plate number using optical character recognition. The proposed method is worked well for Malaysian's private cars plate number, and can be implemented in car park system to increase level of security of the system by confirming the bar code of the parking ticket and the plate number of the car at the incoming and outgoing gates.


1993 ◽  
Vol 5 (2) ◽  
pp. 192-197 ◽  
Author(s):  
Hisashi Kurosaki ◽  
◽  
Makoto Yagi ◽  
Hisanori Yokosuka

A vehicle license number recognition system for measuring travel time has been developed. This is a vehicle license number recognition system by applying image processing. A series of four-digit numbers are transmitted to the center. With the recognition results being matched at the center, the system is capable of directly measuring travel time which is an important parameter for the operation of the road traffic surveillance and control system. This paper will discuss (1) a hardware composition for solving problems associated with the imaging processing to be done outdoors and problems such as processing speed, etc.; (2) the method of extracting each letter by extracting license plate region from complicated images, and the letter recognition system based on peripheral-pattern matching; and (3) experiments for evaluation in the field. As a result of experiments for evaluation, the processing time is within a second, and the recognition rate for all moving vehicles has been as high as over 85% at day time and night time.


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