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United Kingdom License Plate Detection Dataset

The dataset combines annotated license plates from real-world traffic across the UK, offering high-quality images for license plate recognition, license plate detection, and OCR tasks, enabling advancements in autonomous vehicles, traffic management, and smart city systems.

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  • images
    118 798
  • Auto
  • Computer Vision
  • Machine Learning
  • Detection
  • Data annotation
  • Security

The dataset combines annotated license plates from real-world traffic across the UK, offering high-quality images for license plate recognition, license plate detection, and OCR tasks, enabling advancements in autonomous vehicles, traffic management, and smart city systems.

Get in touch Download sample
  • Auto
  • Computer Vision
  • Machine Learning
  • Detection
  • Data annotation
  • Security
  • images
    118 798

Dataset Info

Characteristic Data
Description License plate images with labeling for OCR tasks
Tasks Detection, Classification, OCR
Total number of files 118 798
Marking OCR
Labeling Model, details, plane text, tag, country, number
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Technical
Characteristics

Characteristic Data
Image extensions png
Extension of labeling file csv
Source and collection methodology: Data was collected by parsing videos from various sources

Industries

Automotive Industry

  • Development of autonomous driving systems:

    Using license plate recognition to identify other vehicles and improve navigation.

  • Road object recognition:

    Integrating license plate data into object detection systems to improve accuracy.

  • Improvement of safety systems:

    Automatic detection of stolen vehicles and prevention of unauthorized access.

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Logistics and Transportation

  • Monitoring and managing vehicles:

    Real-time vehicle tracking using license plate recognition.

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FAQs

How was United Kingdom License Plate Detection Dataset collected?
The data collection process involved parsing real-world traffic videos across different urban areas, road types, and traffic conditions. This ensures a dataset that reflects road networks, traffic signals, speed limits, and weather variations commonly encountered in public transportation and driving scenarios.
What types of annotations are provided?
Annotations consist of OCR labeling, vehicle details, and metadata. These annotated license plates provide the foundation for benchmark datasets in car plate detection, traffic analytics, and road safety technologies.
Does the dataset cover different traffic scenarios?
Yes, the dataset encompasses a diverse range of traffic conditions, from urban congestion to smoother road segments. This variation makes it effective for training deep learning algorithms, computer vision systems, and transportation analytics models.
Can I request a sample of the dataset before downloading it?
Yes, Unidata provides samples from the license plate recognition database. This allows you to evaluate image quality, annotation accuracy, and OCR labeling before committing to the full dataset for traffic flows, driving behaviors, or road safety research.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model. Free samples of our datasets are available for testing and evaluation, while full access to the dataset requires purchase.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets, including this one, are created in compliance with GDPR and applicable data protection laws. The data is collected from legally permissible sources, ensuring ethical use in traffic management and license plate recognition systems.
How are Unidata datasets stored?
Unidata datasets are securely stored on AWS cloud infrastructure to guarantee scalability and high availability. The storage and management process complies with ISO 27001 and ISO 27701 standards, ensuring that annotated license plates and OCR labels are protected in a secure and privacy-focused environment.
Is this a real-world dataset or synthetic data?
This is a real-world dataset created by parsing videos of UK roads and transportation systems. Unlike synthetic datasets, it reflects authentic traffic patterns, driving behaviors, and road conditions, making it highly valuable for autonomous driving, urban mobility research, and smart city projects.
Still have questions about using Unidata datasets? Read our user-guides

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