I am trying to classify multi-page documents using a convolutional neural network (CNN). The content of each page in the corpus contains only text (i.e., no photographs or icons), and different documents may have a different shape (height and width). I'd like my classification approach to use all pages in each document, rather than just the first page.
As far as I know, the input to a CNN (like theano) needs a standardized shape. My first thought was to create a single image array that has all the pages concatenated. But I would then have to resize/zero pad all concatenated pages to match the length of the document with the largest page count, and use that as my lowest height and width for the set of concatenated pages. If I don't use this strategy I risk losing resolution on the words in the image, but that is a giant input vector.
I feel like splitting each document's pages into separate samples would be a better approach to standardize input preprocessing, but I'm at a loss for how to classify the whole document if I'm just training on loose pages of each document. Can anyone advise on me on this?