License Plate Detection

Common Voice is an audio dataset that consists of a unique MP3 and corresponding text file. There are 9,283 recorded hours in the dataset. The dataset also includes demographic metadata like age, sex, and accent. The dataset consists of 7,335 validated hours in 60 languages.

This dataset includes 4,500 fully annotated images (over 30,000 license plate characters) from 150 vehicles in real-world scenarios where both the vehicle and the camera (inside another vehicle) are moving. The images were acquired with three different cameras and are available in the Portable Network Graphics (PNG) format with a size of 1,920 × 1,080 pixels. The cameras used were: GoPro Hero4 Silver, Huawei P9 Lite, and iPhone 7 Plus. We collected 1,500 images with each camera, divided as follows: - 900 of cars with gray license plates; - 300 of cars with red license plates; - 300 of motorcycles with gray license plates. The dataset is split as follows: 40% for training, 40% for testing and 20% for validation. Every image has the following annotations available in a text file: the camera in which the image was taken, the vehicle’s position and information such as type (car or motorcycle), manufacturer, model and year; the identification and position of the license plate, as well as the position of its characters. Source: A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

This dataset, called RodoSol-ALPR dataset, contains 20,000 images captured by static cameras located at pay tolls owned by the Rodovia do Sol (RodoSol) concessionaire, which operates 67.5 kilometers of a highway (ES-060) in the Brazilian state of Espírito Santo. There are images of different types of vehicles (e.g., cars, motorcycles, buses and trucks), captured during the day and night, from distinct lanes, on clear and rainy days, and the distance from the vehicle to the camera varies slightly. All images have a resolution of 1,280 × 720 pixels. An important feature of the proposed dataset is that it has images of two different license plate (LP) layouts: Brazilian and Mercosur (to maintain consistency with existing works, we refer to “Brazilian” as the standard used in Brazil before the adoption of the Mercosur standard). Every image has the following information available in a text file: the vehicle’s type (car or motorcycle), the LP’s layout (Brazilian or Mercosul), its text (e.g., ABC-1234), and the position (x, y) of each of its four corners. We labeled the corners instead of just the LP bounding box to enable the training of methods that explore LP rectification, as well as the application of a wider range of data augmentation techniques. Regarding privacy concerns related to our dataset, we remark that in Brazil the LPs are related to the respective vehicles, i.e., no public information is available about the vehicle drivers/owners. Moreover, all human faces (e.g., drivers or RodoSol’s employees) were manually redacted (i.e., blurred) in each image.

This dataset aims at evaluating the License Plate Character Segmentation (LPCS) problem. The experimental results of the paper Benchmark for License Plate Character Segmentation were obtained using a dataset providing 101 on-track vehicles captured during the day. The video was recorded using a static camera in early 2015. The images of the dataset were acquired with a digital camera in Full-HD and are available in the Portable Network Graphics (PNG) format with 1920×1080 pixels each. The average size of each file is 4.08 Megabytes (a total of 8.60 Gigabytes for the entire dataset). In addition, since there are some approaches that track the car to utilize redundant information to improve the recognition results, we decided to make a dataset with multiples frames per car. In this dataset, there are, on average, 19.80 image frames per vehicle (with a standard deviation of 4.14). Source: Benchmark for License Plate Character Segmentation

The ChineseLP dataset contains 411 vehicle images (mostly of passenger cars) with Chinese license plates (LPs). It consists of 252 images captured by the authors and 159 images downloaded from the internet. The images present great variations in resolution (from 143 × 107 to 2048 × 1536 pixels), illumination and background.

Vehicle-Rear is a novel dataset for vehicle identification that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates.

The Caltech Cars dataset consists of 126 rear-view photographs captured within parking lots. These images possess a resolution of 896 × 592 pixels, featuring a solitary vehicle as the primary subject. The acquisitions were made during daylight hours employing a handheld camera at roughly equivalent distances for all instances. "The dataset was presented in a PhD thesis titled 'Unsupervised learning of models for object recognition.'"

• 5 domains: synthetic domain, document domain, street view domain, handwritten domain, and car license domain * over five million images

The PKU dataset has almost 4,000 images categorized into five groups (G1-G5) that show different situations. For example, G1 has images of highways during the day with only one car in them. On the other hand, G5 has images of crosswalks during the day or at night with multiple cars and license plates (LPs). It can be used to train and test LP detectors, as the authors labeled the position of each visible LP on each image.

CD-HARD comprises 102 images featuring vehicles with oblique license plates sourced from the Cars dataset. Each image within this dataset exclusively depicts a single vehicle and was captured during daylight hours. While the dataset encompasses images from diverse geographic regions, it predominantly consists of images seemingly taken in European locales. The dataset was presented in the 'License Plate Detection and Recognition in Unconstrained Scenarios' research paper.

The CLPD dataset comprises 1200 images that encompass various regions within mainland China. These images were sourced from diverse origins, including the internet, mobile devices, and in-car recording devices. While the majority of the images were recorded during daylight hours, a portion of them were captured at nighttime. The dataset predominantly features passenger cars, with a limited number of images depicting trucks and buses. The dataset was presented in the research paper titled "A Robust Attentional Framework for License Plate Recognition in the Wild."

Description: <a href="https://gts.ai/dataset-download/nomeroff-russian-license-plates/" target="_blank">👉 Download the dataset here</a> This dataset consists of high-resolution photographs of Russian vehicle license plates captured in diverse environments and lighting conditions. It is designed for image classification and object detection tasks, particularly aimed at automatic license plate recognition (ALPR) systems. The dataset features vehicles in urban, rural, and highway settings, with a wide range of vehicle models and plate formats that adhere to Russian vehicle registration standards. Download Dataset Dataset Overview: A collection of JPG images with varying resolutions, capturing license plates from multiple angles (frontal, side, rear) and distances. Environments include daytime and nighttime scenes, varying weather conditions (sunny, rainy, snowy), and different lighting (natural and artificial). CSV Columns: file_name: The name of the image file (str, unique values). plate_number: The alphanumeric license plate number present in the image (str). vehicle_type: The type of vehicle (e.g., sedan, SUV, truck, etc.) – this allows for classification beyond just license plate recognition (str). angle_of_view: The angle at which the image was captured (e.g., frontal, side, rear) – this provides additional context for training models to handle various perspectives (str). weather_condition: The weather conditions during image capture (e.g., sunny, rainy, snowy, etc.), offering insights into how weather impacts recognition performance (str). time_of_day: Whether the image was captured during the day, evening, or night, useful for models dealing with varying lighting conditions (str). occlusion_level: A numeric rating (0-3) representing the level of obstruction on the license plate (e.g., due to dirt, damage, or angle), helping models adapt to real-world imperfections (int). Applications: License Plate Recognition (LPR) Systems. Vehicle Tracking and Identification. Traffic Monitoring and Enforcement. Smart City Solutions and Automated Toll Systems. This dataset is sourced from Kaggle.