Table Detection and Extraction Using Deep Learning

My area of work on the project has been the core file-reading and file-creation stuff so I haven't had much of a chance to review the layout algorithm performance across documents.

Having been working on a purely rules-based approach in a private repository for a side project it seems like the algorithms the contributor has implemented get you a lot closer to the correct result than starting from rules alone but it definitely feels like adding some context-aware rules would get all the way there. I'm not sure whether they'd be in scope for the layout analysis project itself or someone could take the open-core and extend it, as I was attempting in my side project.

The current layout analysis algorithms don't do much normalization as far as I'm aware, the Recursive-XY Cut algorithm uses page level font-size information [0] to tune parameters but it doesn't infer a common structure or format either as an input or result.

The aim of most layout analysis algorithms is to produce classifications for regions, e.g. paragraphs, titles, lists which I suppose counts as denormalizing the document? Arriving at those classifications generally relies on first splitting the document into sections or regions and then classifying those regions. So far the implemented algorithms mainly focus on the first step, splitting a document into discrete regions. An example of the second step using ML approaches to classify those regions by the same contributor can be found here [1].

With the rule based approaches I've been experimenting with you can use certain information from the common producers to normalize certain features. For example line spacing and font size have a well defined relationship, as do whitespace size and font size (though this is a fuzzier relationship and goes out the window entirely for justified text).

An example where you rely on non-locality to parse a document, in this SEC filing there are both key values and a table: https://www.sec.gov/Archives/edgar/data/1428796/000110465920...

For the values following the subheading "Institutional Investment Manager Filing this Report:" the left hand column are keys for the right hand values.

At the bottom of the document there's a table containing the columns "Form 13F File Number" and "Name".

Now you could use a couple of rules to infer the difference between the key-values and the table:

1) The keys in a key value list end in ':'.

2) The keys in a key value list have a different font/color to the values.

Both of those rules hold true here but not in all or even most documents. For this reason you need to use the whole page to deduce the type of these sections, rather than immediately surrounding features/pixels as an ML algorithm might.

[0]: https://github.com/UglyToad/PdfPig/blob/master/src/UglyToad....

[1]: https://github.com/BobLd/PdfPigMLNetBlockClassifier

There really is no real differentiation between formatting and content in a PDF, so it's not possible to truly separate them.

But by its nature text is intrinsically semantic. I am just surprised that a document format utterly free of semantics has lasted so long. Perhaps because we (as people and organizations) can't agree on the structure of documents?

Another way to see this, perhaps, is as the failure of the promise of xml and the ecosystem around it? In the late 90's many of us thought that all documents would be xml for content and styling would be through xslt or even it's big sister, xsl. Well, THAT went nowhere despite all the W3C meetings and papers.

It's interesting you brought up graphics as an analogy. It's true that you can have graphics which are literally just lines and that's adequate for many needs. However, modern CAD drawing systems increasingly use notions of 2D/3D objects and a disciplined series of transformations. They call it "parametric modeling" and it's where where all drawing consist of a series of transformations that can be represented in a timeline. I suspect modern parametric model CAD can very much be semantic.