Entity Extraction within our Text Analysis API will extract mentions of people, places etc. - http://aylien.com/entity-extraction

You can sign up and use it for free @ http://developer.aylien.com/signup

Very cool use of ST library.

Hey, I haven't tried your papers, but we do use SDMGR which is using both visual and textual features. Before it, we were using visual only, but SDMGR is way better. Check it out, from the mmocr package. It takes OCR (or in your case, if the PDFs are nicer, raw texts), and for each text block:

1. takes some sort of text embedding - via LSTM branch

2. takes visual features (i think pretrained resnet), for the center pixel.

3. Several graph layers, where you have every node to every node attention.

4. Finally, node classification - in your case you would classify categories such as "total", "date", "income type"

We are not using it, but it does support edge classification as well, so your edges might be {'None', 'Belong to same line-tuple', 'Belong to same total-tuple'}. I'm not sure what you do if you have e.g. Node1, Node2, Node3, and edge(1,2) = 0.8, edge(2,3)=0.9, edge(1,3)=0.1 though...

Please do let me know if you try out multiple papers. We stuck with the first thing that worked reasonably well, and I don't have the time to play around; but if I knew for sure one was better, it would be nice.