大数据时代的车牌汉字识别

doi:10.3969/j.issn.1007-2861.2015.04.019

Abstract

Abstract:

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%.

Key words: deep belief network, deep learning, license plate of Chinese character recognition, restricted Boltzmann machine

SHEN Wenfeng, ZHANG Jianlei, ZHOU Dingqian, CHEN Shengbo, QIU Feng. Recognition of Chinese characters on license plates based on big data[J]. Journal of Shanghai University（Natural Science Edition）, 2016, 22(1): 88-96.

References

[1] 王笑京, 沈鸿飞, 汪林. 中国智能交通系统发展战略研究[J]. 交通运输系统工程与信息, 2006(4): 9-12.
[2] 吴佳. 车辆牌照识别系统的设计与实现[D]. 北京: 北京交通大学, 2015.
[3] 中华人民共和国公安部. GA36—2014 中华人民共和国公共安全行业标准: 中华人民共和国机动车号牌[S]. 北京: 中国标准出版社, 2014.
[4] Wu F, Wang Y G, Hou X W. License plate character recognition based on framelet [C]// International Conference on Wavelet Analysis and Pattern Recognition. 2007: 673-676.
[5] Cheng R, Bai Y P. A novel approach for license plate slant correction, character segmentation and Chinese character recognition [J]. International Journal of Signal Processing Image Processing and Pattern Recognition, 2014, 7(1): 353-364.
[6] 汪启伟. 图像直方图特征及其应用研究[D]. 合肥: 中国科学技术大学, 2014.
[7] 蔺海峰, 马宇峰, 宋涛. 基于SIFT特征目标跟踪算法研究[J]. 自动化学报, 2010, 36(8): 1204-1208.
[8] Ciresan D, Meier U, Masci J, et al. A committee of neural networks for traffic sign classification [C]//International Joint Conference on Neural Networks. 2011: 1918-1921.
[9] Google Tessact [EB/OL]. [2015-10-19]. http://sourceforge.net/projects/tesseract-ocr/.
[10] Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798-1828.
[11] Bengio Y. Learning deep architectures for AI [M]//Jordan M. Foundations and trends in machine learning. Boston: Now Publishers Inc, 2009: 1-127.
[12] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets [J]. Neural Computation, 2006, 18(7): 1527-1554.
[13] Hinton G E. Deep belief networks [J]. Scholarpedia, 2009, 4(5): 5947.
[14] Fischer A, Igel C. Training restricted Boltzmann machines: an introduction [J]. Pattern Recognition, 2014, 47(1): 25-39.
[15] Le R N, Bengio Y. Representational power of restricted Boltzmann machines and deep belief networks [J]. Neural Computation, 2008, 20(6): 1631-1649.
[16] 刘忠, 茆诗松. 分组数据的Bayes分析——Gibbs抽样方法[J]. 应用概率统计, 1997(2): 211-216.

Recognition of Chinese characters on license plates based on big data

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