A method for an artificial neural network, the method comprising: providing the artificial neural network, wherein the artificial neural network is trained to reduce the confidence of the artificial neural network in classifying inputs outside a desirable input distribution by: training the artificial neural network using a first set of inputs and associated labels, the labels correctly classifying the corresponding inputs, wherein the inputs within the first set are within the desirable input distribution of the artificial neural network, wherein the inputs within the first set comprises images of road vehicle number plates; and training the artificial neural network using a second set of inputs and associated labels, wherein the inputs within the second set are outside of the desirable input distribution of the artificial neural network, wherein the inputs within the second set comprise images of one or more predetermined signs, other than number plates, known to be affixed to road vehicles, and wherein the labels within the second set comprise randomly assigned classifications; and feeding one or more inputs through the artificial neural network to determine classifications, classifying the inputs, and confidence values associated with the classifications. providing the artificial neural network, wherein the artificial neural network is trained to reduce the confidence of the artificial neural network in classifying inputs outside a desirable input distribution by: training the artificial neural network using a first set of inputs and associated labels, the labels correctly classifying the corresponding inputs, wherein the inputs within the first set are within the desirable input distribution of the artificial neural network, wherein the inputs within the first set comprises images of road vehicle number plates; and training the artificial neural network using a second set of inputs and associated labels, wherein the inputs within the second set are outside of the desirable input distribution of the artificial neural network, wherein the inputs within the second set comprise images of one or more predetermined signs, other than number plates, known to be affixed to road vehicles, and wherein the labels within the second set comprise randomly assigned classifications; and training the artificial neural network using a first set of inputs and associated labels, the labels correctly classifying the corresponding inputs, wherein the inputs within the first set are within the desirable input distribution of the artificial neural network, wherein the inputs within the first set comprises images of road vehicle number plates; and training the artificial neural network using a second set of inputs and associated labels, wherein the inputs within the second set are outside of the desirable input distribution of the artificial neural network, wherein the inputs within the second set comprise images of one or more predetermined signs, other than number plates, known to be affixed to road vehicles, and wherein the labels within the second set comprise randomly assigned classifications; and feeding one or more inputs through the artificial neural network to determine classifications, classifying the inputs, and confidence values associated with the classifications. <EOS>