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The openalpr project is pretty good, but it uses an older approach with lot of hand-coded logic instead of the convolutional neural net approach of this project. openalpr is faster than this, at least now, but I think the neural net has potential to be faster and more accurate.

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The author could take a camera on the road and shoot pics for a day or two, then use OpenALPR to predict the plates and check this dataset to see how accurate it is. If it's good enough, it can be used to train a CNN model with real images of cars and no manual labeling.

I started saving file from my dashcam several months ago with that idea - to use the openalpr results as training data for a neural net, but the results I'm getting from openalpr aren't good enough to use without a lot of manual review.

Hard to tell. Unfortunately neither this nor the openalpr project have any benchmarks or evaluations on a test set. It could be that the openalpr project already gives you 99% accuracy (unlikely), in which case there really isn't a point in trying to optimize it more (e.g. move on to harder problems).

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