Interesting. Two engineers at Apple worked on something similar that would slurp character bounding boxes from a PDF page and reconstruct paragraphs, columns, tables, etc.

It was surfaced in iOS a decade ago as "tap to zoom" feature for PDFs. It's funny — as with a lot of things there was a lot of sophisticated engineering under the hood and then marketing simply wants it to detect a tap in a paragraph and zoom to its bounds.

I can't think of the last time I read a PDF on my phone or I would test it to see if it still works as I remember.

2022, and we need this in browser reader modes.

An interesting problem, particularly with handwritten documents. And not limited to what they worked on, from their conclusion: "...the number of clustering levels is not limited to two, since document layout tasks are extremely diverse in nature. Paragraphs can further be clustered into text columns or sections, which may belong to even higher level blocks. Figure 16 shows a physical paragraph, or part of a semantic paragraph which spans across multiple text columns..." Not to mention text wrapping around figures.

The general field is called "document structure analysis" or "document layout analysis." There's been lots of work--at a cursory glance at this article, I'm not sure they've discussed that literature.

I worked on a similar problem a decade or so, although our work was done mostly by hand. We were trying to not only read in (bilingual) dictionaries using OCR, but turn them into dictionary entries, and then parse each entry into its parts (headword, part of speech, definitions or glosses, example sentences, subentries...). I won't go into details, but to our surprise one of the most difficult parts for the machine to get right was recognizing bold or italicized text.

Only two weeks ago, I was trying to save an online-only book of 24 chapters. The text is filled with images to help illustrate and contextualize the content. I individually saved each chapter as PDFs and ran a few different command line tools to try to extract the contents to plain text. They all came out badly disjointed. Even tools that were meant to do what this paper describes failed miserably at reconstructing naturally flowing text.

While this isn't something I need on a regular basis, it's timely news to hear about someone making progress on what seems like it ought to be a straightforward problem to solve. As the results of my efforts show, it must not be nearly as simple as one might expect.

Would this be of help with YouTubes auto-generated subtitles? I hate the fact the words appear just in time one by one, unlike traditional complete sentences that appear fully one at a time.

If you are a fan of irony, the paper is also available in PDF format.

Dupe: https://news.ycombinator.com/item?id=45323027

remotely related, but I have yet to find a solution for page classification in a document for tables, i.e. a classifier that returns the index of pages containing tables in a document that is reliable

solutions using things like img2table or pymupdf are really bad (pymupdf is not even reliable for text pdfs)