[ECCV 2018] CCPD: a diverse and well-annotated dataset for license plate detection and recognition

RodoSol-ALPR: a dataset for license plate detection and recognition that includes 20K images of vehicles with Brazilian/Mercosur license plates.

UFPR-ALPR: a dataset for license plate detection and recognition that includes 4,500 fully annotated images acquired in real-world scenarios where both the vehicle and the camera (inside another vehicle) are moving.

This project is used to detect the license plate of the vehicle in real time, trained using Car Detection Licence Plate dataset available on Kaggle. Used yolov4 because it performs much better than traditional cv techniques and then used EasyOCR to extract text from the number plate. Please see readme for details.

UC3M License Plate detection and recognition dataset

A New Benchmark Dataset for Egyptian License Plate Detection and Recognition

YOLOv11n → YOLOv11x models fine-tuned for license plate detection using the Roboflow dataset. Includes .pt and .onnx model files, sample output images, and a comprehensive README for usage via Python, CLI, and deployment. Supports both edge devices and cloud GPUs for flexible integration across environments.

This project presents a License Plate Recognition (LPR) system using YOLO models for object detection and EasyOCR for text recognition, designed to achieve real-time, accurate license plate detection. Trained on a diverse, hybrid dataset of over 52,000 labeled images.

Detect and recognize vehicle license plates using YOLOv8 for precise detection and CRNN for accurate character recognition. This project leverages annotated datasets to train models for efficient vehicle image analysis and license plate identification. Achieved an evaluation accuracy of 0.8567 for character recognition using CRNN.

Supplementary materials of "Simultaneous End-to-End Vehicle and License Plate Detection with Multi-Branch Attention Neural Network", including two re-annotated datasets: DETROIT and DOC.

A dataset to train deep neural license plate detector

Data for automatic license plate character recognition and detection algorithms

Custom YoloV4 Darknet/Tensorflow model for license plate detection on the AMD-Xilinx Kria KV260 Vision-AI starter Kit. Utilize transfer learning to create your own custom object detecion model on a custom dataset, quantize and compile in Vitis-AI for easy deployment and evaluation on FPGA.

Dataset of Indian license plate photos for computer vision. This dataset can be used by any object detection frameworks like YOLO or SSD. However some work is necessary to reformat the dataset.

Bangadeshi Vehicle dataset for Automatic License Plate Detection and Recognition.

A lightweight YOLOv8-based license plate detection script that preprocesses images, resizes them to training resolution, runs inference, and saves the detection results with bounding boxes. Designed for Vietnamese license plate datasets, fully customizable, and easy to integrate into larger ALPR pipelines.

License plate detection model using YOLOV8, trained on License Plate Recognition Image Dataset from Roboflow

License plate detection yolo model trained using dataset from kaggle

ELP: a diverse and well-annotated dataset for license plate detection and recognition

A dataset for ship license plate detection, including shore-based surveillance and drone-captured images

Project for the 2024/2025 Computer Vision course. License plate detection with YOLOv5 and recognition using CRNN and reimplemented PDLPR on CCPD dataset.

"An end-to-end YOLOv9-based model for real-time Iranian License Plate Detection. Includes dataset conversion to YOLO format, training pipeline, and webcam inference."

A self-contained pipeline for detecting and recognizing vehicle license plates in images. This project combines YOLO for plate detection and PaddleOCR for text recognition, and provides tools to evaluate performance against annotated datasets.

A three-phase workflow covering dataset preparation, database-backed feature engineering, deep learning (CNN & Faster R-CNN), and OCR evaluation for robust license plate detection and recognition.

This project implements Automatic License Plate Recognition (ALPR) using YOLOv8 for vehicle and plate detection, combined with OCR for text extraction. It works on both videos and image datasets, generating annotated outputs and CSV reports for intelligent transport and security applications.

This project implements a custom object detection model to detect Qatari license plates using YOLOv8. It leverages the Roboflow API to download and manage datasets and uses the Ultralytics YOLOv8 library for training and evaluation.

YOLOv8, a state-of-the-art object detection model, excels in speed and accuracy, identifying objects it is trained to recognize. Pre-trained on the COCO dataset, it detects common objects like people and vehicles but lacks the ability to identify license plates. To address this, the model is fine-tuned with a labeled dataset for 100 epochs, enablin

This is an Ultralytics-based abnormal target detection project. Its primary function is to recognize fire, smoke, license plates, and faces, and to send corresponding alarm information and on-site images to a host computer. I provide detailed annotations, multiple models and datasets. You can select different models based on your specific needs.

This project focuses on creating a Deep Learning model with CNN layers using the architecture of YOLO model from scratch, then training the model on the dataset and fine-tuning it for accurate detection of vehicle license plates in various environments. The codebase includes data preprocessing, model training scripts, and evaluation pipelines.

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