Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Mar 19, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep learning algorithms for license plate recognition: A review.

Laixiang Xu¹, Zihan Shang¹, Xiangjun Chen²

¹School of Computer and Artificial Intelligence, Henan University of Urban Construction, Pingdingshan, Henan, 467036, China.

Neural Networks : the Official Journal of the International Neural Network Society

|March 18, 2026

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

ZDAM: a new deep learning model for bean leaf disease diagnosis.

Frontiers in plant science·2026

Same author

Exploring chemical space based on Transformation to design broad-spectrum 3CL<sup>pro</sup> inhibitors against coronavirus.

European journal of medicinal chemistry·2026

Same author

Winter-associated downregulation of ovarian NR5A2 correlates with impaired follicle development in the striped hamster (Cricetulus barabensis).

Scientific reports·2026

Same author

Ag-Based Schottky-Engineered MOF Sonosensitizers Delivered via Dissolvable Microneedles for Sonodynamic Biofilm Eradication and Wound Healing.

ACS applied materials & interfaces·2026

Same author

TAFNet: Trusted Multiview Associative Fusion Neural Networks for Analyzing Dynamic Brain Networks.

IEEE transactions on neural networks and learning systems·2026

Same author

Arc Erosion and Wear Induced Particle Emissions in C/Cu Tribo-Pairs of Pantograph-Catenary System.

Materials (Basel, Switzerland)·2026

Same journal

Anchor-based disentanglement framework for incremental multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Complex-valued amplitude-phase interference modeling for adversarially robust classification.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026

See all related articles

This review clarifies license plate recognition (LPR) technology evolution, highlighting deep learning

Area of Science:

Intelligent Transportation Systems (ITS)
Computer Vision
Machine Learning

Background:

License plate recognition (LPR) is vital for intelligent transportation systems (ITS) and vehicle management.
Rapid advancements present diverse technologies, inconsistent standards, and unclear future trends.
A systematic review is needed to clarify evolution, evaluate methods, and identify research gaps.

Purpose of the Study:

To systematically review and analyze the evolution of license plate detection and recognition technologies.
To evaluate the efficacy and limitations of traditional and deep learning-based LPR methods in complex scenarios.
To identify current challenges and future research directions in LPR for ITS.

Main Methods:

Systematic literature review and analysis of LPR technologies.

Keywords:

Deep learning Dynamic scenes Intelligent transportation License plate recognition

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Related Experiment Videos

Last Updated: Mar 19, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Collating and characterizing public mainstream license plate image datasets.

Summarizing the evolution from traditional feature engineering methods to deep learning frameworks.

Main Results:

Deep learning significantly enhances LPR robustness over traditional methods, especially in complex conditions.
Key challenges include illumination variation, adverse weather, multi-angle views, non-standard plates, low resolution, and character adhesion.
Identified limitations in current algorithms and performance assessment standards.

Conclusions:

Future directions include lightweight networks, multi-scale features, cross-regional universal models, and incorporating traffic-scene priors.
Cross-modal methods like video-stream analysis and multi-sensor fusion show promise for dynamic scene recognition.
The review provides a theoretical reference for optimizing and innovating LPR technology in ITS.