license-plate-recognition

Self-hosted, local only NVR and AI Computer Vision software. With features such as object detection, motion detection, face recognition and more, it gives you the power to keep an eye on your home, office or any other place you want to monitor.

License Plate Detection and Recognition in Unconstrained Scenarios

A cross-platform video structuring (video analysis) framework. If you find it helpful, please give it a star: ) 跨平台的视频结构化（视频分析）框架，觉得有帮助的请给个星星 : )

Pytorch Implementation For LPRNet, A High Performance And Lightweight License Plate Recognition Framework.

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).

Automatic License Plate Recognition (ALPR) or Automatic Number Plate Recognition (ANPR) software that works with any camera.

PLPR utilizes YOLOv5 and custom models for high-accuracy Persian license plate recognition, featuring real-time processing and an intuitive interface in an open-source framework.

yolov7 车牌检测 车牌识别 中文车牌识别 检测 支持双层车牌 支持12种中文车牌

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.

A python program that uses the concept of OCR using machine learning to identify the characters on a Nigerian license plate

Automatic Number Plate Detection YOLOv8

A computer based NVR (Network Video Recorder) with AI capabilities

Open Source and Free License Plate Recognition Software

ALPR model in unconstrained scenarios for Chinese license plates

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.

Lightweight & fast OCR models for license plate text recognition.

A License-Plate detecttion application based on YOLO

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