python做神经网络识别车牌_利用神经网络建立一个自动车牌识别系统

最新推荐文章于 2021-03-31 00:43:38 发布

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最新推荐文章于 2021-03-31 00:43:38 发布

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文章标签： python做神经网络识别车牌

本文链接：https://blog.youkuaiyun.com/weixin_29518761/article/details/113505089

本文档介绍了一个实验性的自动车牌识别系统，该系统利用神经网络进行开发。虽然不适用于实际应用，但可供有兴趣深入研究的人参考。项目依赖OpenCV和NumPy库，并提供了训练模型、生成测试图像和检测图像中车牌的步骤。要改进项目，可以处理Issues页面上列出的增强功能。

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Deep ANPR

Using neural networks to build an automatic number plate recognition system. See this blog post for an explanation.

Note: This is an experimental project and is incomplete in a number of ways, if you're looking for a practical number plate recognition system this project is not for you. If however you've read the above blog post and wish to tinker with the code, read on. If you're really keen you can tackle some of the enhancements on the Issues page to help make this project more practical. Please comment on the relevant issue if you plan on making an enhancement and we can talk through the potential solution.

Usage is as follows:

./extractbgs.py SUN397.tar.gz: Extract ~3GB of background images from the SUN database into bgs/. (bgs/ must not already exist.) The tar file (36GB) can be downloaded here. This step may take a while as it will extract 108,634 images.

./gen.py 1000: Generate 1000 test set images in test/. (test/ must not already exist.) This step requires UKNumberPlate.ttf to be in the fonts/ directory, which can be downloaded here.

./train.py: Train the model. A GPU is recommended for this step. It will take around 100,000 batches to converge. When you're satisfied that the network has learned enough press Ctrl+C and the process will write the weights to weights.npz and return.

./detect.py in.jpg weights.npz out.jpg: Detect number plates in an image.

The project has the following dependencies:

OpenCV

NumPy

Different typefaces can be put in fonts/ in order to match different type faces. With a large enough variety the network will learn to generalize and will match as yet unseen typefaces. See #1 for more information.