摘要
在大数据时代,交通信息成为网络数据量最大的数据来源之一,智能交通成为必然需求.车牌识别是智能交通的基础,可广泛应用于车库管理、交通监控等工程中,然而识别的准确率还有待加强,已有算法对于字母、数字的识别准确率都非常高,而对于中国特有的汉字识别却效果不佳.提出用受限玻尔兹曼机组成的深信度网络算法来识别车牌字符,大大提升了汉字识别的准确率,使准确率达到99.44%.
Today, traffic provides sources of huge scale data sets on the network, calling for the development of intelligent traffic. The license plate recognition （LPR） techniques are an important basis of intelligent traffic, and widely applied in applications such as garage management and traffic monitoring. However, the current LPR algorithms are imperfect in terms of recognition accuracy. Although working well in recognizing English letters and digits, they are unsatisfactory in recognizing Chinese characters. This paper proposes a license plate recognition algorithm using a deep belief network （DBN） algorithm consisting of restricted Boltzmann machines （RBM）. It greatly improves the quality of Chinese character recognition with accuracy rate up to 99.44%.
出处
《上海大学学报（自然科学版）》
CAS
CSCD
北大核心
2016年第1期88-96,共9页
Journal of Shanghai University:Natural Science Edition
基金
上海市科委资助项目(14DZ2261200)
关键词
车牌汉字识别
深信度网络
受限玻尔兹曼机
深度学习
license plate of Chinese character recognition
deep belief network
restricted Boltzmann machine
deep learning
