  Learning-based algorithms for automated license plate recognition implicitly
assume that the training and test data are well aligned. However, this may not
be the case under extreme environmental conditions, or in forensic applications
where the system cannot be trained for a specific acquisition device.
Predictions on such out-of-distribution images have an increased chance of
failing. But this failure case is oftentimes hard to recognize for a human
operator or an automated system. Hence, in this work we propose to model the
prediction uncertainty for license plate recognition explicitly. Such an
uncertainty measure allows to detect false predictions, indicating an analyst
when not to trust the result of the automated license plate recognition. In
this paper, we compare three methods for uncertainty quantification on two
architectures. The experiments on synthetic noisy or blurred low-resolution
images show that the predictive uncertainty reliably finds wrong predictions.
We also show that a multi-task combination of classification and
super-resolution improves the recognition performance by 109\% and the
detection of wrong predictions by 29 %.
