Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

Exogenous boron induced cell expansion and carbohydrate allocation, conferring leaf succulence and salt resistance in sugar beet.

Plant physiology and biochemistry : PPB·2026
Same author

A deep learning framework for the localization of landmarks on the lateral semi circular canals.

PloS one·2026
Same author

Sensorineural hearing outcomes in HPV-positive oropharyngeal cancer: a secondary analysis of the TROG 12.01 randomized trial (SHOUT).

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2026
Same author

Correction: Strive for excellence: unboxing the antecedents of art design students' creativity in the learning process in China.

Frontiers in psychology·2026
Same author

Strive for excellence: unboxing the antecedents of art design students' creativity in the learning process in China.

Frontiers in psychology·2026
Same author

Dietary Spirulina Ameliorates Arsenic-Induced Toxicity in Nile Tilapia, Oreochromis niloticus.

Veterinary medicine and science·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Dec 17, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.5K

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication.

Kh Tohidul Islam1,2, Ram Gopal Raj1, Syed Mohammed Shamsul Islam3,4

  • 1Department of Artificial Intelligence, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia.

Sensors (Basel, Switzerland)
|July 1, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient automatic vehicle license plate recognition method using computer vision. The proposed system balances high accuracy and low processing time for real-time intelligent transportation systems.

Keywords:
artificial neural networksautomatic license plate recognitionhistogram of oriented gradientsintelligent vehicle access

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.5K
A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

6.9K

Related Experiment Videos

Last Updated: Dec 17, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.5K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.5K
A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

6.9K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Intelligent Transportation Systems

Background:

  • Automatic vehicle license plate recognition (ALPR) is crucial for intelligent vehicle access control and monitoring.
  • Increasing vehicle numbers necessitate effective real-time ALPR systems.
  • Existing computer vision methods face challenges in balancing accuracy and processing speed.

Purpose of the Study:

  • To propose a novel method for license plate recognition that optimizes both accuracy and processing time.
  • To develop a system suitable for real-time applications in intelligent vehicle monitoring.

Main Methods:

  • A two-stage approach: detection and recognition.
  • Detection stage: Image processing to identify the region of interest (license plate).
  • Recognition stage: Feature extraction using Histogram of Oriented Gradients (HOG) and training an artificial neural network (ANN) for character identification.

Main Results:

  • The proposed method demonstrated high accuracy in license plate recognition.
  • Achieved significantly low processing time compared to existing techniques.
  • Experimental results confirm the method's suitability for real-time applications.

Conclusions:

  • The developed license plate recognition method effectively balances accuracy and processing speed.
  • The system is well-suited for real-time intelligent vehicle access control and monitoring.
  • This approach addresses the need for efficient ALPR in increasingly congested traffic environments.