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# High Accuracy Chinese Plate Recognition Framework
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### 介绍
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This research aims at simply developping plate recognition project based on deep learning methods, with low complexity and high speed. This
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project has been used by some commercial corporations. Free and open source, deploying by Zeusee.
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HyperLPR是一个使用深度学习针对对中文车牌识别的实现，与较为流行的开源的[EasyPR](https://github.com/liuruoze/EasyPR)相比，它的检测速度和鲁棒性和多场景的适应性都要好于目前开源的EasyPR。
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### 更新
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+ 添加端到端的序列识别模型识别率大幅度提升
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+ 添加的端到端模型可以识别 新能源车牌，教练车牌，白色警用车牌
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+ 更新Windows版本的Visual Studio 2015 工程（2017.11.15）
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+ 增加cpp版本,目前仅支持标准蓝牌(需要依赖OpenCV 3.3)
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+ 添加了简单的Android实现 (骁龙835 (*720*x*1280*) 200ms)
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### 特性
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+ 速度快 720p ，单核 Intel 2.2G CPU (macbook Pro 2015)平均识别时间低于100ms
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+ 基于端到端的车牌识别无需进行字符分割
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+ 识别率高 EasyPR数据集上0-error达到 95.2%, 1-error识别率达到 97.4%
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+ 轻量 总代码量不超1k行
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### 注意事项:
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+ Win工程中若需要使用静态库，需单独编译
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+ 本项目的C++实现和Python实现无任何关联，都为单独实现
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+ 在编译C++工程的时候必须要使用OpenCV 3.3(DNN 库)，否则无法编译
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### Python 依赖
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+ Keras (>2.0.0)
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+ Theano(>0.9) or Tensorflow(>1.1.x)
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+ Numpy (>1.10)
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+ Scipy (0.19.1)
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+ OpenCV(>3.0)
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+ Scikit-image (0.13.0)
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+ PIL
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### CPP 依赖
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+ Opencv 3.3
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### 设计流程
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> step1. 使用opencv 的 HAAR Cascade 检测车牌大致位置
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>
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> step2. Extend 检测到的大致位置的矩形区域
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>
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> step3. 使用类似于MSER的方式的 多级二值化 + RANSAC 拟合车牌的上下边界
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>
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> step4. 使用CNN Regression回归车牌左右边界
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>
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> step5. 使用基于纹理场的算法进行车牌校正倾斜
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>
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> step6. 使用CNN滑动窗切割字符
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>
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> step7. 使用CNN识别字符
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### 简单使用方式
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```python
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from hyperlpr import pipline as pp
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import cv2
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image = cv2.imread("filename")
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image,res = pp.SimpleRecognizePlate(image)
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print(res)
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```
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### Linux/Mac 编译
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+ 仅需要的依赖OpenCV 3.3 (需要DNN框架)
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```bash
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cd cpp_implementation
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mkdir build
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cd build
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cmake ../
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sudo make -j
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```
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### 可识别和待支持的车牌的类型
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- [x] 单行蓝牌
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- [x] 单行黄牌
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- [x] 新能源车牌
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- [x] 白色警用车牌
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- [x] 使馆/港澳车牌
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- [x] 教练车牌
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- [ ] 双层黄牌
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- [ ] 双层武警
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- [ ] 双层军牌
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- [ ] 农用车牌
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- [ ] 民航车牌
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- [ ] 个性化车牌
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### 测试样例
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#### Android示例
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### 识别测试APP
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- 体验 Android APP：[https://fir.im/HyperLPR](https://fir.im/HyperLPR)
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### 数据分享
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车牌识别框架开发时使用的数据并不是很多，有意着可以为我们提供相关车牌数据。联系邮箱 455501914@qq.com。
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### 获取帮助
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+ HyperLPR讨论QQ群：673071218, 加前请备注HyperLPR交流。
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