https://github.com/matthewearl/deep-anpr

Using neural networks to build an automatic number plate recognition system
https://github.com/matthewearl/deep-anpr

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Using neural networks to build an automatic number plate recognition system

• Host: GitHub
• URL: https://github.com/matthewearl/deep-anpr
• Owner: matthewearl
• License: mit
• Created: 2016-05-02T16:29:33.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2019-11-14T22:40:30.000Z (about 5 years ago)
• Last Synced: 2024-10-29T17:39:50.989Z (about 1 month ago)
• Language: Python
• Size: 41 KB
• Stars: 1,843
• Watchers: 119
• Forks: 699
• Open Issues: 99
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Deep ANPR

Using neural networks to build an automatic number plate recognition system.

See [this blog post](http://matthewearl.github.io/2016/05/06/cnn-anpr/) 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:

1. `./extractbgs.py SUN397.tar.gz`: Extract ~3GB of background images from the [SUN database](http://groups.csail.mit.edu/vision/SUN/)

   into `bgs/`. (`bgs/` must not already exist.) The tar file (36GB) can be [downloaded here](http://vision.princeton.edu/projects/2010/SUN/SUN397.tar.gz).

   This step may take a while as it will extract 108,634 images.

2. `./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](http://www.dafont.com/uk-number-plate.font).

3. `./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.

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

The project has the following dependencies:

* [TensorFlow](https://tensorflow.org)

* 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](https://github.com/matthewearl/deep-anpr/issues/1) for more information.