At the moment, I'm making a comparative analysis between two different neural networks for a class. The problem they are both trying to solve is detecting tables on documents. Below is an example of one:
One of the algorithms I am analyzing is a fully convolutional network. The way the algorithm works is that it goes pixel by pixel and assigns each pixel a label of which class it could be in (in my case, table or not a table).
However, my issue is how should you pre-process/label your data if you're training one of these networks? Because tables themselves have words on them, looking at such a fine resolution to detect tables is not optimal. It would be preferable if I could have the FCN look for structural elements that help us identify tables (big gaps, columns, header, etc).
I'm not sure how to train a network that goes pixel by pixel to search for the bigger structural elements. Any thoughts on this?