Ideally, I would like to get all the information from the page. Phone numbers, who is suing whom, case caption, etc...
With OCR, you get bits and pieces of information, but because I don't know what the type of the document is, it is difficult to determine where, structurally speaking, this information resides on a page.
If I could use AI to determine the type of the doc, I would know the structure of the document and I could then use OCR to pinpoint specific information on the page.
Most court documents are created from templates.
If your OCR is unable to recognize the characters on a full page, I don't think it when scanning a region either?
Unless using full resolution of a full image is somehow too much for the algorithm in use.
But then I'd just subdivide the entire image into regions, and scan them all independently. This is also a trivially parallelizable task, so you can throw many servers at it, if the time to get results is an issue.
OCR is a better understood problem than a general neural net, so I think it's likely easier to improve its quality that to superseded the quality with image-based recognition.