基于单字符注意力的全品类鲁棒车牌识别

复杂场景下的高精度车牌识别仍然存在着许多挑战, 除了光照、分辨率不可控和运动模糊等因素导致的车牌图像质量低之外, 还包括车牌品类多样产生的行数不一和字数不一等困难, 以及因拍摄角度多样出现的大倾角等问题. 针对这些挑战, 提出了一种基于单字符注意力的场景鲁棒的高精度车牌识别算法, 在无单字符位置标签信息的情况下, 使用注意力机制对车牌全局特征图进行单字符级特征分割, 以处理多品类车牌和倾斜车牌中的二维字符布局问题. 另外, 该算法通过使用共享参数的多分支结构代替现有算法的串行解码结构, 降低了分类头参数量并实现了并行化推理. 实验结果表明, 该算法在公开车牌数据集上实现了超越现有算法的精度, 同时具有较快的识别速度.

Full-category Robust License Plate Recognition Based on Character Attention

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 multi-category 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.