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https://github.com/szad670401/end-to-end-for-chinese-plate-recognition

多标签分类,端到端的中文车牌识别基于mxnet, End-to-End Chinese plate recognition base on mxnet
https://github.com/szad670401/end-to-end-for-chinese-plate-recognition

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多标签分类,端到端的中文车牌识别基于mxnet, End-to-End Chinese plate recognition base on mxnet

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# end-to-end-for-plate-recognition
多标签分类,端到端的中文车牌识别基于mxnet .
从[xlvector的ocr代码](https://github.com/szad670401/learning-dl/tree/master/mxnet/ocr)修改,减少了参数,由于我没有显卡。单线程 9 samples/s 速度 ,用CPU在MBP上跑了50w张样本。识别率到了81%。不过还没有完全收敛。

## 训练好的模型
https://github.com/ibyte2011/end-to-end-for-chinese-plate-recognition

## 关于车牌识别
生成的车牌对于实际车牌并不是效果很好,在结合真实样本和GAN,训练了一个更好的模型,对真实车牌表现很好。
并实现了一整套车牌识别的系统命名为HyperLPR https://github.com/zeusees/HyperLPR

## 依赖:
+ Numpy
+ Mxnet
+ Opencv

## 生成的车牌样张
通过渲染车牌加上畸变、噪声、与自然环境结合生成车牌的样本。

![image](./recognize_samples/00.jpg)
![image](./recognize_samples/01.jpg)
![image](./recognize_samples/02.jpg)
![image](./recognize_samples/03.jpg)
![image](./recognize_samples/04.jpg)
![image](./recognize_samples/06.jpg)
![image](./recognize_samples/07.jpg)
![image](./recognize_samples/08.jpg) ![image](./recognize_samples/02.jpg)
![image](./recognize_samples/09.jpg)
![image](./recognize_samples/10.jpg)
![image](./recognize_samples/11.jpg)
## 识别样张


## Author
+ Jack Yu
+ Xiao Xiao