Random-Positioned License Plate Recognition Using Hybrid Broad Learning System and Convolutional Networks
Random-Positioned License Plate Recognition Using Hybrid Broad Learning System and Convolutional Networks
复制标题
使用混合广泛学习系统和卷积网络的随机定位车牌识别
DOI：
10.1109/tits.2020.3011937
复制
发表时间：
2022-01-01
影响因子：
8.5
通讯作者：
Wang, Bingshu
中科院分区：
文献类型：
--
作者：
Chen, C. L. Philip;Wang, Bingshu
本文提出了一种将全卷积网络与广义学习系统相结合的车牌识别框架。提出了一种融合多尺度和层次特征的像素级两类分类方法--全卷积网络，用于随机定位目标检测。对于字符分割，训练的AdaBoost级联分类器被用来定位代表行政区域的关键字符。设计了一种基于对称区域水平投影的车牌倾斜角度估计方法和一种基于无连字符垂直投影的车牌倾斜角度估计方法。在字符识别方面，提出了一种基于映射特征节点的堆叠式自动编码器的广义学习系统，并分别对字母和数字进行了识别。在澳门车牌上的实验表明，该方法优于一些最先进的方法。通过将该方法应用于其他地区或国家，可以预期其兼容性和通用性。
This paper proposes a framework combing a fully convolutional network with broad learning system for license plate recognition. The fully convolutional network, which is designed as a pixel-level two-class classification method, is proposed for random-positioned object detection by the fusion of multi-scale and hierarchical features. For character segmentation, a trained AdaBoost cascade classifier is employed to locate a key character representing for an administrative area. We design a symmetric region horizontal projection method to estimate the license plate slant angles, and an approach based on vertical projection without hyphens to solve the problem of touching characters. For character recognition, the broad learning system with stacked auto-encoder of mapped feature nodes is proposed, and two structures are explored to recognize letters and digits, respectively. Experiments conducted on Macau license plates show that the proposed method outperforms some state-of-the-art approaches. The compatibility and generality can be expected by applying the proposed method to other regions or countries.