License plate (LP) location is a key technology in the process of license plate recognition (LPR). How to realize the LP location and character segmentation of complex vehicle images has always been a hot issue in the research of intelligent transportation systems. In this paper, a novel LP location method combining the connected domain (CD) slope detection method and the dynamic template matching method is proposed for the complicated situations that the tilt angle of the LP is too large, the LP is defaced, the LP frame is unclear and the characters are adhesive or defective. Firstly, the method uses the CD slope detection method to find out the equal-slope CD of the LP characters in the optimal segmentation image after pre-processing and determine the tilt angle. Then locate the horizontal region of the LP and realize the tilt correction. After that, the proposed the dynamic template matching method is used to locate the vertical position of the LP and segment the LP characters accurately. Finally, the experiment proves that the proposed algorithm reduces the recognition difficulty of the LP with the above problems, and has the characteristics of fast speed, accurate recognition, good adaptability and strong anti-interference. It also has good versatility and scalability for the newly introduced new energy LP with 8-character.
License plate segmentation is a key technology in the process of license plate location and recognition. How to realize automatic segmentation of license plate image under complex illumination conditions has been a hot issue in intelligent transportation system (ITS). This paper deals with license plate image segmentation under a variety of lighting conditions. Based on the adaptive segmentation of license plate images by the Pulse Coupled Neural Network (PCNN), the relationship between the license plate image contrast and the PCNN iteration entropy is analyzed. An adaptive segmentation algorithm for license plate image using Deep Neural Network (DNN) to select the optimal result is proposed, and the selected segmentation image is filtered by the connected domain, which lays a foundation for subsequent license plate location, character segmentation and recognition. Simulation experiments show that the proposed algorithm performs better license plate segmentation and optimal selection for license plate images under various lighting conditions.