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

yolov5 车牌检测 车牌识别 中文车牌识别 检测 支持12种中文车牌 支持双层车牌

License Plate Detection and Recognition in Unconstrained Scenarios

works in real-time with detection and recognition accuracy up to 99.8% for Chinese license plates: 100 ms/plate

World's fastest ANPR / ALPR implementation for CPUs, GPUs, VPUs and NPUs using deep learning (Tensorflow, Tensorflow lite, TensorRT, OpenVX, OpenVINO). Multi-Charset (Latin, Korean, Chinese) & Multi-OS (Jetson, Android, Raspberry Pi, Linux, Windows) & Multi-Arch (ARM, x86).

A two stage lightweight and high performance license plate recognition in MTCNN and LPRNet

Fast Automatic License Plate Recognition (ALPR) framework.

License Plate Detection using YOLOv8

license plate detection and recognition

Detects license plate of car and recognizes its characters

Hobby project to track vehicles that are over speeding and violating red light

The project developed using TensorFlow to recognize the License Plate from a car and to detect the charcters from it.

This repository provides you with a detailed guide on how to build a real-time license plate detection and recognition system. The source code of the repository implemented on Jetson Nano reached 40 FPS.

Automatic Number (License) Plate Recognition using Tensorflow Object Detection API

Open Source and Free License Plate Recognition Software

Machine Learning APIs for common use cases, include: General OCR (Simplified/Traditional Chinese), Custom OCR, Image Similarity, Object Recognition, Face Detection, Face Comparison, Human Image Segmentation, Human Attribute Recognition, Pornography Detection, Image Super Resolution, Text Similarity, Car License Plate, etc.

The main objective of this project is to identify overspeed vehicles, using Deep Learning and Machine Learning Algorithms. After acquisition of series of images from the video, trucks are detected using Haar Cascade Classifier. The model for the classifier is trained using lots of positive and negative images to make an XML file. This is followed by tracking down the vehicles and estimating their speeds with the help of their respective locations, ppm (pixels per meter) and fps (frames per second). Now, the cropped images of the identified trucks are sent for License Plate detection. The CCA (Connected Component Analysis) assists in Number Plate detection and Characters Segmentation. The SVC model is trained using characters images (20X20) and to increase the accuracy, 4 cross fold validation (Machine Learning) is also done. This model aids in recognizing the segmented characters. After recognition, the calculated speed of the trucks is fed into an excel sheet along with their license plate numbers. These trucks are also assigned some IDs to generate a systematized database.

ALPR model in unconstrained scenarios for Chinese license plates

The ultimate customizable dash-cam platform, with ALPR and object recognition capabilities

This project using yolo3 to detection license plate in street

A License-Plate detecttion application based on YOLO

iranian car license plate recognition with yolo object detection and cnn lstm ctc loss

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

Generate the fake Chinese license plate images for detection & recognition

深度学习车牌检测与识别，检测结果包含车牌矩形框和4个角点，基于pytorch框架运行

A project where the license plate number is extracted from image of a vehicle using Object detection and Character recognition techniques.

本课题利用 CCPD 数据及自生成的高清车牌数据，基于加入注意力机制以及轻型卷积改良的 YOLO 目标检测算法检测车牌，同时利用自定义的 ResNet+Transformer 网络构成字符识别算法用于识别检测出的车牌内容信息。所设计的模型提升了平均精度（mAP）值，降低了计算复杂度和模型体积。此外，本课题开发了一款基于 Gradio 框架搭建的 BS 结构车牌识别系统，能实现 Web 端与移动端界面自适应，完美适配不同设备屏幕。

In this project we utilize OpenCV t in order to identify the license number plates and the python pytesseract for the characters and digits extraction from the plate. As well this project will presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector. We build Web App with a Python program that automatically recognizes the License Number Plate by the end of this journi. The results have shown that the trained neural network is able to perform with high accuracy of nearly 90-95 percent in recognizing license plates in low resolution images using this system.

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.

Code to Blur Human Faces and Vehicle License Plates in Video and Images using a SoTA Object Detection model YOLOv8