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Cascade Classifier Training: Train different object variants together or separate?

I am trying to build a cascade classifier for license plate detection. So far I managed to get some good results, but could be better.

Looking at all license plates that should be recognized, there are some similarities (all are rectangular and with some letters on them), but other than that they differ between countries and sometimes there are various "categories" inside the same country even.

My question is: Should I train for every country and type of license plate separately and then trying to combine the classifiers later or am I likely to get better/same results when training one classifier with the complete set of license plates?