{"id":19938811,"url":"https://github.com/raphaelbs/cnn-anpr","last_synced_at":"2025-07-02T08:35:29.429Z","repository":{"id":91345849,"uuid":"161035597","full_name":"raphaelbs/cnn-anpr","owner":"raphaelbs","description":"ANPR built with a convolutional neural network. 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With a large enough variety the network will learn to generalize and\nwill match as yet unseen typefaces. See\n[#1](https://github.com/matthewearl/deep-anpr/issues/1) for more information.\n\nYou can install all required packages using:\n\n\u003e pip install -r ./requirements.txt\n\n# Usage\n\n1. `./extractbgs.py SUN397.tar.gz`: Extract ~3GB of background images from the [SUN database](http://groups.csail.mit.edu/vision/SUN/)\n   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).\n   This step may take a while as it will extract 108,634 images.\n\n2. `./gen.py 1000`: Generate 1000 test set images in `test/`. (`test/` must not\n    already exist.) This step requires some font in the\n    `fonts/` directory. You can download the UK version\n    [here](http://www.dafont.com/uk-number-plate.font).\n\n3. `./train.py`: Train the model. A GPU is recommended for this step. It will\n   take around 100,000 batches to converge. When you're satisfied that the\n   network has learned enough press `Ctrl+C` and the process will write the\n   weights to `weights.npz` and return.\n\n4. `./detect.py in.jpg weights.npz out.jpg`: Detect number plates in an image.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraphaelbs%2Fcnn-anpr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fraphaelbs%2Fcnn-anpr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraphaelbs%2Fcnn-anpr/lists"}