Strategy to create a testbed and gold standard
You can't.  You can't squeeze blood out of a stone.  If you want to evaluate how well your method will work on real documents, you need real documents.  With synthetically/artificially generated documents, you'll never know if they have the same characteristics as real documents.

You don't necessarily need a standard benchmark that others have used -- but you do need to assemble a representative set of real documents to evaluate on.  Basically, if there is no standard benchmark in your field, you'll have to assemble one yourself.

Yes, this is difficult to do.  This is one of the less-well-known and less-glamorous aspects of working on machine learning: in practical projects, often we spend 80% of our time (or more!) just assembling data sets, and only 20% on the actual learning algorithms themselves.