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car_plate's Introduction

这是一个在不分割字符的情况下直接识别车牌号码的简单项目。

使用方法:

第一步:生成训练和测试数据集
    cd generateCarPlate
    python3 genCarPlate.py

将会在data目录下生成train、test数据集。
生成的图片示例:
沪KTKLTZ
津GMQGF7
渝M885B9
蒙ZUTK8T
生成的图片已经做了旋转、模糊、灰度变化等图像增强。git

训练卷积only的模型

网络模型:

model

训练
    cd pytorch_model
    python3 train.py 30 0.0001

30是在训练集训练的次数,0.0001是学习速率

测试
    python3 test.py

将会输出准确率,我训练的car_plate_javer.pt模型能达到98.2的准确率(由于训练和测试数据集都是随机生成的,因此可能不同人测试有差异)。 这个准确率不算高,由于我的笔记本算力有限,没能进一步训练更大、更好的模型,不过我想,这个项目已足以证明不分割直接识别车牌的可行性

训练crnn(双向gru)+ctc

网络模型:

model

训练
    cd pytorch_model
    python3 train.py 30 0.0001 10

30是在训练集训练的次数,0.0001是学习速率,10是batch的大小

测试
    python3 test.py

Pull Request

Pull request is welcome.

communicate with

QQ group: 704153141

致谢

数据集生成使用了genCarPlate,感谢作者!

License

BSD 3-Clause

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