Vehicle License Plate Localization and License Number Recognition Using Unit-Linking Pulse Coupled Neural Network

Zhao, Yongqiang; Gu, Xiaodong

doi:10.1007/978-3-642-34500-5_13

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2019

2024

Publication Types

Select...

Article2

Book1

Other1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

License Plate Recognition Using Artificial Neural Network Techniques

Aruna,

Ravi,

Suruthi

2024

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

License Plate Recognition Using Artificial Neural Network Techniques

Aruna,

Ravi,

Suruthi

2024

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Research and Application of License Plate Recognition Technology Based on Deep Learning

Yao

Zhao

Fan

et al. 2019

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

There are many types of vehicle license plates in China, including new energy license plates, large truck license plates, government vehicle license plates, and military license plates. The existing commercial license plate recognition system only targets common license plates and does not completely cover the full range of license plates. Therefore, this paper proposes an SSD-based end-to-end license plate recognition system (LPR-SSD). The LPRSSD network architecture consists of upper and lower classification networks: the upper layer network is used for vehicle license detection and classification, and the lower layer network is used for license plate character detection and classification. In order to enhance the generalization performance of the LPR-SSD network, in addition to the real license plate image captured by the camera, this paper synthesizes 50K simulated license plates for each type of license plate according to the legal document [1]. Experiments show that LPR-SSD achieved a faster convergence speed during training. After the test set verification, the accuracy of license plate location detection and classification reaches 98.3%, and the character recognition accuracy rate reaches 99.1%.

show abstract

PCNN Mechanism and its Parameter Settings

Deng

Yan

2020

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

Vehicle License Plate Localization and License Number Recognition Using Unit-Linking Pulse Coupled Neural Network

Cited by 4 publications

References 10 publications

License Plate Recognition Using Artificial Neural Network Techniques

License Plate Recognition Using Artificial Neural Network Techniques

Research and Application of License Plate Recognition Technology Based on Deep Learning

PCNN Mechanism and its Parameter Settings

Contact Info

Product

Resources

About