Integration of Deep Learning and IndustrialComputer Vision Library for License Plate Recognition

• Main Authors: WU, TING-YU, 吳定餘
• Other Authors: LIAO, HSIEN-CHOU
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/m94425

碩士 === 朝陽科技大學 === 資訊工程系 === 107 === At present, the license plate recognition (LPR) technique is gradually maturing and widely used in daily life. License plate detection and optical character recognition are two basic steps. Many studies simply utilize deep learning technique, or traditional computer vision technique to realize LPR. Therefore, deep learning technique and industrial computer vision library (Euresys Open eVision) are attempted to be integrated in this study. The proposed approach can be divide into three stage: the first stage is to apply deep learning to locate the license plate in the image. The second stage is license plate correction. It will transform license plate image to the image with expected angle. Finally, it will recognize the license plate by splitting the individual character. In the experimental study, the accuracy of the implemented system can reach 96.7% with an average execution time of 77.2ms. It shows the proposed approach can achieve a high accuracy in the practical environment.