Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Using neural networks to build an automatic number plate recognition system
| Date | Stars |
|---|---|
| 2026-07-31 | 1859 |
| 2026-08-01 | 1858 |
| 2026-08-06 | 1858 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# 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.
Excerpt of 2,052 characters
Read on GitHub
39
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:486880901f32b886, llm:description: "Using neural networks to build an automatic number plate recognition system"
matched fp:486880901f32b886, llm:description: "Using neural networks to build an automatic number plate recognition system"