Learning-based algorithms for automated license plate recognition implicitly assume that the training and test data are well aligned. However, this may not be case under extreme environmental conditions, or in forensic applications where system cannot trained a specific acquisition device. Predictions on such out-of-distribution images have an increased chance of failing. But failure is oftenti...