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High Performance Chinese License Plate Recognition Framework.
| Date | Stars |
|---|---|
| 2026-07-24 | 6225 |
| 2026-07-25 | 6226 |
| 2026-07-28 | 6226 |
| 2026-07-30 | 6226 |
| 2026-08-06 | 6226 |
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## HyperLPR3 - High Performance License Plate Recognition Framework.
#### [](https://pypi.org/project/hyperlpr3/)[](https://pypi.org/manage/project/hyperlpr3/releases/)[](https://jitpack.io/#HyperInspire/hyperlpr3-android-sdk)
[中文文档](README_CH.md)
### Demo APP Installation
- Android APP：[Scan QR Code](http://fir.tunm.top/hyperlpr)
### Quick Installation
`python -m pip install hyperlpr3`
###### support：python3, Windows, Mac, Linux, Raspberry Pi。
###### 720p cpu real-time (st on MBP r15 2.2GHz haswell).
#### Quick Test
```bash
# image url
lpr3 sample -src https://koss.iyong.com/swift/v1/iyong_public/iyong_2596631159095872/image/20190221/1550713902741045679.jpg
# image path
lpr3 sample -src images/test_img.jpg -det high
```
#### How to Use
```python
# import opencv
import cv2
# import hyperlpr3
import hyperlpr3 as lpr3
# Instantiate object
catcher = lpr3.LicensePlateCatcher()
# load image
image = cv2.imread("images/test_img.jpg")
# print result
print(catcher(image))
```
#### Start the WebAPI service
```bash
# start server
lpr3 rest --port 8715 --host 0.0.0.0
```
Path to open SwaggerUI after startup：[http://localhost:8715/api/v1/docs](http://localhost:8715/api/v1/docs) View and test the online Identification API service:
#### Q&A
Q：Whether the accuracy of android in the project is consistent with that of apk-demo？
A：Please compile or download the Android shared library from the release and copy it to Prj-Android for testing。
Q：Source of training data for license plates？
A：Since the license plate data used for training involves legal privacy and other issues, it cannot be provided in this project. Open more big data sets [CCPD](https://github.com/detectRecog/CCPD) registration dataset。
Q：Provision of training code？
A：The resources provide the old training code, and the training methods for HyperLPR3 will be sorted out and presented later。
#### Resources
- [HyperLPR3车牌识别-五分钟搞定: 中文车牌识别光速部署与使用](https://blog.csdn.net/weixin_40193776/article/details/129258107)
- [HyperLPR3车牌识别-Android-SDK光速部署与使用](https://blog.csdn.net/weixin_40193776/article/details/129394240)
- [HyperLPR3车牌识别-Linux/MacOS使用：C/C++库编译](https://blog.csdn.net/weixin_40193776/article/details/129295679)
- [HyperLPR3车牌识别-Android使用：SDK编译与部署](https://blog.csdn.net/weixin_40193776/article/details/129354938)
- To be added... Contributions welcome
#### Other Versions
- [HyperLPRv1](https://github.com/szad670401/HyperLPR/tree/v1)
- [HyperLPRv2](https://github.com/szad670401/HyperLPR/tree/v2)
### TODO
- Support for rapid deployment of WebApi services
- Support multiple license plates and double layers
- Support large Angle license plate
- Lightweight recognition model
### Specialty
- 720p faster, single core Intel 2.2G CPU (MaBook Pro 2015) average recognition time is less than 100ms
- End-to-end license plate recognition does not require character segmentation
- The recognition rate is high, and the accuracy of the entrance and exit scene is about 95%-97%
- Support cross-platform compilation and rapid deployment
### Matters Need Attention:
- The C++ and Python implementations of this project are separate
- When compiling C++ projects, OpenCV 4.0 and MNN 2.0 must be used, otherwise it will not compile
- Android project compilation ndk as far as possible to use version 21
### Python Dependency
- opencv-python (>3.3)
- onnxruntime (>1.8.1)
- fastapi (0.92.0)
- uvicorn (0.20.0)
- loguru (0.6.0)
- python-multipart
- tqdm
- requests
### Cross-platform support
#### Platform
- Linux: x86、Armv7、Armv8
- MacOS: x86
- Android: arm64-v8a、armeabi-v7a
#### Embedded Development Board
- Rockchip: rv1109rv1126(RKNPU)
### CPP Dependency
- Opencv 4.0 above
- MNN 2.0 above
### C/C++ Compiling DepExcerpt of 10,647 characters
Read on GitHub
Jack Yu
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Jingyu Yan
90
7
Hao
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马思 · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:331f9b59fc621306, topic:deep-learning, topic:tensorflow