Soft Computing

, Volume 22, Issue 7, pp 2403–2419 | Cite as

Convolutional neural networks-based intelligent recognition of Chinese license plates

Methodologies and Application


License plate recognition has gained extensive applications in many fields. Some interesting algorithms and models have been developed to deal with the issues in the location, segmentation and recognition processes. This paper focuses on the intelligent recognition of Chinese license plates with daily life backgrounds by designing new convolutional neural networks. Firstly, to extract Chinese license plates from the images subject to daily life backgrounds, which is more difficult than from those with fixed background, a color edge algorithm is proposed to detect specific edges of input image. A color-depressed grayscale conversion method is presented to preprocess plate samples with poor quality, and an improved relocation method is given to eliminate plate frames. Then a combination of connected component analysis and projection analysis is implemented for the segmentation. Finally, simplified and recurrent convolutional neural networks are designed to automatically recognize the characters (the first one is Chinese character, which is followed by six alphanumeric characters). A total of 2189 images containing Chinese license plates are collected manually with different backgrounds. Tested on these samples, the location rate of \(98.95\%\), segmentation rate of \(96.58\%\) and recognition rate of \(98.09\%\) are, respectively, achieved by our algorithms. The accuracy rate of recognition of Chinese license plates reaches \(93.74\%\), and it averagely takes 318 ms to complete the recognition of a license plate, which meets the real-time processing requirement.


Chinese license plate recognition Color edge Connected component analysis Simplified convolutional neural network Recurrent convolutional neural network 



This study was funded by the Qing Lan Project of Jiangsu Province, the National Natural Science Foundation of China under Grant Nos. 61273122 and 61573106, and the National Priority Research Project NPRP 8-274-2-107.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of Electronics and Information EngineeringSoochow UniversitySuzhouPeople’s Republic of China
  2. 2.School of MathematicsSoutheast UniversityNanjingPeople’s Republic of China
  3. 3.Department of MathematicsKing Abdulaziz UniversityJeddahSaudi Arabia
  4. 4.Texas A&M University at QatarDohaQatar

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