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Car License Plate Detection Dataset

The license plate dataset contains vehicle images with annotated license plates, enabling accurate recognition of number plates across various traffic conditions and road environments

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  • Million images
    2.6
  • Countries
    86
  • Computer Vision
  • Data annotation
  • Detection
  • Auto
  • Machine Learning
  • Security
  • Million images
    2.6
  • Countries
    86

Dataset Info

Characteristic Data
Description Car license plate images with labeling for OCR tasks
Data types Image
Tasks Detection, Classification, OCR
Total number of files 2,663,532
Marking OCR
Labeling Model, details, plate text, tag, country
Countries Poland, Germany, France, Ukraine, Spain, United Kingdom, Hungary, Netherlands, USA, Italy, Belgium, China, Belarus, Turkey, Serbia, Lithuania, Kazakhstan, Bulgaria, Romania, Austria, Switzerland, Slovakia, Czech Republic, USSR, Latvia, Norway, Croatia, Vietnam, Canada, Greece, Indonesia, Finland, Georgia, Estonia, UAE, Israel, Thailand, Uzbekistan, Sweden, Slovenia, Luxembourg, Ireland, Portugal, Moldova, Guernsey, Non-recognized and partially recognized states, Denmark, Malta, Azerbaijan, Armenia, Kyrgyzstan and etc.
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Statistics

Distribution by continent

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

Dataset Use Cases

  • Transportation & Logistics

    Fleet Tracking and Toll Automation

    This car license plate dataset supports the development of AI-powered systems for automated fleet monitoring, toll collection, and delivery verification. By training models on annotated license plates in varied lighting, weather conditions, and traffic scenarios, logistics companies can ensure accurate license plate recognition across diverse road networks and urban areas.



  • Smart City Infrastructure

    Traffic Management and Parking Automation

    Urban planners and technology providers use this license plate detection database to improve traffic flows, monitor traffic density, and automate parking access. The dataset enables integration with intelligent traffic systems, traffic signals, and parking control platforms, supporting better urban mobility and road safety in real-world conditions.



  • Law Enforcement & Security

    Vehicle Identification and Access Control

    Police, customs, and private security agencies use this car plate detection dataset for number plate recognition, stolen vehicle recovery, and border security. Its precise bounding box and polygon annotations allow accurate vehicle identification even in challenging traffic patterns and weather conditions.



  • Autonomous Vehicles & AI Research

    Real-Time Detection for Self-Driving Systems

    Autonomous driving developers rely on this license plate recognition database to train models for detecting and interpreting vehicle registrations in real-time traffic. By encompassing diverse road types, speed limits, and driving behaviors, the dataset ensures better performance in both simulated and real-world deployments.



What is the Car License Plate Detection Dataset used for?
The dataset is designed for license plate recognition, OCR tasks, and autonomous driving research. It supports detection, classification, and number plate recognition models, helping improve traffic management, smart city systems, and vehicle registration monitoring.
What types of annotations are provided?
Each image in the dataset contains OCR-based annotations, including plate text, vehicle model details, tags, and country information.
How was the data for this dataset collected?
The data was sourced from diverse real-world traffic flows, urban areas, and road conditions. Images were gathered across multiple countries and road types, ensuring broad coverage for traffic scenarios, vehicle registrations, and intelligent transportation research.
What should I consider before buying Car License Plate Detection Dataset?
Before purchasing, evaluate your project goals, such as character recognition, plate detection, or vehicle registration systems. Review the data diversity, country coverage, and annotation details, as these factors impact the performance of learning models, recognition technology, and real-time traffic applications.
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