This could work, but it is important to note that a lot of ML algorithms trained in a closed domain (no "other" class) will be pretty bad at knowing what they don't know. This is an open problem in ML.

Choosing the threshold will be hard. And (as mentioned by other poster) the model is unlikely to generalize well to classes of data it has not seen. I suspect that this approach will get things similar to numbers wrong quite often, like handwritten characters (a,b,c). Including these into the training set is much more likely to yield a model which will successfully discriminate it.