基于单字符注意力的全品类鲁棒车牌识别

There are still many challenges for high-precision vehicle license plate recognition in complex scenarios. In addition to the low quality of license plate images caused by factors such as poor illumination, low resolution, and motion blur, challenges also include different variant numbers of characters and lines for different license plate categories, as well as large inclination caused by the various camera locations. In response to these challenges, this paper proposes a scene-robust high-precision license plate recognition algorithm based on character attention, which performs character level segmentation on the global feature map of the license plate images without character position label information. Such character level segmentation can deal with the 2D character layout problems in multicategory license plates and inclined license plates. In addition, this algorithm uses a shared weight classification header structure to replace the serial decoding structure used in existing algorithms, which reduces the number of classification header parameters and realizes parallel inference. The experimental results show that the algorithm achieves high accuracy which surpasses the existing algorithms on the public-domain data sets, and meanwhile has a faster recognition speed.