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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 on deep learning methods, with low complexity and high speed. This
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project has been by some commercial corporations. Free and open source, deploying by Zeusee.
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HyperLPR是一个基于Python的使用深度学习针对对中文车牌识别的实现，与开源的[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EasyPR。
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### 特性
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+ 单张720p图像，单核Intel 2.2G CPU 识别时间140ms左右，比EasyPR单核识别速度快近10倍的时间。
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+ 识别率在EasyPR数据集上0-error达到70.2% 1-error识别率达到 89.6%
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+ 检测方法在实时性、召回率、准确率上都优于MSER方法，检测recall和easyPR持平。
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+ 代码框架轻量，总代码不到1k行。
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### 依赖
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+ Keras + Theano backend (Tensorflow data order)
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+ Theano
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+ Numpy
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+ Scipy
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+ OpenCV
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+ scikit-image
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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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> step2. Extend 检测到的大致位置的矩形区域
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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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> step4. 使用CNN Regression回归车牌左右边界
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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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> step6. 使用CNN滑动窗切割字符
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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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```
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### 测试样例
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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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