I am just getting started with my first simple digit classifier, so my doubts are at a pretty low level. In every dataset of digit images I've seen so far, different variants of each digit are grouped together, for example:
All of these images represent the number 1, but are fairly different in looks. Won't simple convolutional neural networks have a hard time learning the visual pattern for 1 in such a case? Especially considering how the third image is similar to 7 in design.
My questions are these: Would it be better to create other labels such as "1", "1-alt", "1-serif" etc? The CNN can then add the probabilities of the image being a variant of 1 and then give its prediction, but I'm not sure about this.
How do professional classifiers approach this problem?
Theoretically, will this method affect performance or accuracy in any way?