Performing semantic segmentation on images on word documents
There are many papers on document structure analysis.  I suggest you read them, rather than trying to reinvent the wheel.  As they say, a week in the lab can save a day in the library.

I think there may be some misconceptions.  The way we evaluate neural networks is by trying them on a representative workload to see how well they perform.  There are effectively no useful methods for performing a "comparative analysis".  When you ask "how should you..", the answer with neural networks is typically "whatever works".  There are no rules.  Often helpful advice is to look for papers that have addressed this problem before and see what they do.

A plausible approach is to just feed in the entire image as input to the network, with no pre-processing (other than the standard pre-processing for images, i.e., to normalize pixels to mean 0 and variance 0).

Labels are more challenging, and that depends on what task exactly that you want to solve.  Presumably, you need to manually label the images and then use those as your labels.

How would you train such a network?  The same way you train any other network: you construct a training set, choose a loss function, and then use an optimizer to minimize the loss on that training set.