Don't understand classification equation for hard margin SVM
Very good question. Its worth understanding following concepts about SVM to understand about your point  


SVM get trained from the training dataset in such a way that for every instance Xi in dataset
W.Xi + b &lt;= -1 if Xi belongs to -ve class
W.Xi + b >=1 if Xi belongs to +ve class
This also means that for edge instances of +ve and -ve classes the value will be 1 and -1 respectively. This is what it means by making the street as wide as possible.
Above condition is not necessarily true with new unseen instance 'U' because it hasn't been seen by SVM algorithm yet so it can also be inside the street separating the classes
Above equation also implies that the edge instances are separated by 2 units (whatever the unit is). To make the street gap 2 unit, W not give projection of Xi perpendicular to the street but it also scales it to make the gap 2 units. This means W is not necessarily a unit vector.
In other word we are not comparing the component of U in direction perpendicular to the street but scaled version of it so that the street is always 2 units wide


This is intuition based explanation of SVM but mathematically also above equation is the constrain of the optimization problem needed to solved for SVM