Abstract
This paper studies the license plate recognition problem under the complex background and the license plate tilt. Existing methods cannot solve these problems well. This paper proposes an end-to-end rectification network based on deep learning. The model contains three parts: Rectification network, residual module and sequence module, which are responsible for distortion of license plate rectification, image feature extraction and license plate character recognition. In the experiments, we studied the effects of complex backgrounds such as light, rain and snow, and the inclination and distortion of license plates on the accuracy of license plate recognition. The experimental part of this article uses the Chinese Academy of Sciences CCPD dataset, which covers a variety of license plate data in natural scenes. The experimental results show that compared with the existing license plate recognition algorithm, the algorithm in this paper improves significantly the accuracy, and it averages 7.7% in complex scenarios of CCPD dataset.
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