It’s a long shot, but I think if you took DeepPanel (see github), and instead of training it on comic book panels, you set up a training dataset with PDF tables, it would generate the same kind of masks/heatmaps it generates for comic book panels, but for PDF tables (this gives you an image that represents where “table lines” are, and that removes all text and other random stuff, allowing you to process only the table lines).

Then from there, you could scan the image vertically first, doing an average of the pixel of each line of the heatmap to detect where “lines” are, and cut the table into rows. Then once you have the rows, you do the same on each row to get the columns/cell.

I do this for comic book panels and it works very well, I see no reason why it wouldn’t work for PDF tables.

It’s a lot of work but I’m fairly certain it’d work.

Then once you have the cells, it’s just a matter of OCR (you could even maybe try llava for that, I suspect it might work).

Tell me if you need help with this/more details about how I did it for comic books/how I would do it for PDF tables.

nougat by Facebook is your best bet.

Are your pdf random documents from users ? If yes then it will a problem since there can be many ways the pdfs are structured depending on whatever tool was used. If all the pdfs are the same, like created by the same tool then maybe you have a chance, I would inspect the pdf layout and see if there are consistent and then maybe with a pdf library you can get the data (maybe you could use parts of pdf.js from mozila)

i use pdfplumber