PDF to Text, a challenging problem

Back in... 2006ish? I got annoyed with being unable to copy text from multicolumn scientific papers on my iRex (an early ereader that was somewhat hackable) so dug a bit into why that was. Under the hood, the pdf reader used poppler, so I modified poppler to infer reading order in multicolumn documents using algorithms that tessaract's author (Thomas Breuel) had published for OCR.

It was a bit of a heuristic hack; it was 20 years ago but as I recall poppler's ancient API didn't really represent text runs in a way you'd want for an accessibility API. A version of the multicolumn select made it in but it was a pain to try to persuade poppler's maintainer that subsequent suggestions to improve performance were ok - because they used slightly different heuristics so had different text selections in some circumstances. There was no 'right' answer, so wanting the results to match didn't make sense.

And that's how kpdf got multicolumn select, of a sort.

Using tessaract directly for this has probably made more sense for some years now.

This is life. So many times I’ve finished a project and thought to myself: “Now I am an expert at doing this. Yet I probably won’t ever do this again.” Because the next thing will completely in a different subject area and I’ll start again from the basics.

I built an auto HQ solver with tesseract when HQ was blowing up over thanksgiving (HQ was the gameshow by the vine people with live hosts). I would take a screenshot of the app during a question, share it/send it to a little local api, do a google query for the question, see how many times each answer on the first page appeared in the results, then rank the answers by probability.

Didn't work well/was a very naive way to search for answers (which is prob good/idk what kind of trouble I'd have gotten in if it let me or anyone else who used it win all the time), but it was fun to build.

Not that I'm privy to your mind, but it probably was tesseract (and this is my exact experience too...although for me it was about 12 years ago).

No different than a fire ant whose leaf got knocked over by the wind and it moved on to the next.

When you use PDF.js from Mozilla to render a PDF file in DOM, I think you might actually get something pretty close. For example I suppose each Tj becomes a <span> and each TJ becomes a collection of <span>s. (I'm fairly certain it doesn't use <canvas>.) And I suppose it must be very faithful to the original document to make it work.

Try with cpdf (disclaimer, wrote it):

  cpdf -output-json -output-json-parse-content-streams in.pdf -o out.json

  cpdf -j out.json -o out.pdf

Have a look at this notebook[0], not exactly what you're looking for but does provide a 'live' inspector of the various drawing operations contained in a PDF.

[0]: https://observablehq.com/@player1537/pdf-utilities

There's also #4, reliable OCR and semantics extraction that works across many diverse classes of documents, which is relevant for accessibility.

This is hard because:

1. Unlike a business workflow which often only deals with a few specific kinds of documents, you never know what the user is going to get. You're making an abstract PDF reader, not an app that can process court documents in bankruptcy cases in Delaware.

2. You don't just need the text (like in traditional OCR), you need to recognize tables, page headers and footers, footnotes, headings, mathematics etc.

3. Because this is for human consumption, you want to minimize errors as much as possible, which means not using OCR when not needed, and relying on the underlying text embedded within the PDF while still extracting semantics. This means you essentially need two different paths, when the PDF only consists of images and when there are content streams you can get some information from.

3.1. But the content streams may contain different text from what's actually on the page, e.g. white-on-white text to hide information the user isn't supposed to see, or diacritics emulation with commands that manually draw acute accents instead of using proper unicode diacritics (LaTeX works that way).

4. You're likely running as a local app on the user's (possibly very underpowered) device, and likely don't have an associated server and subscription, so you can't use any cloud AI models.

5. You need to support forms. Since the user is using accessibility software, presumably they can't print and use a pen, so you need to handle the ones meant for printing too, not just the nice, spec-compatible ones.

This is very much an open problem and is not even remotely close to being solved. People have been taking stabs at it for years, but all current solutions suck in some way, and there's no single one that solves all 5 points correctly.

>replace their OCR pipelines with Flash for a fraction of the cost of previous solutions, it's really quite remarkable.

As someone who had to build custom tools because VLMs are so unreliable: anyone that uses VLMs for unprocessed images is in for more pain than all the providers which let LLMs without guard rails interact directly with consumers.

They are very good at image labeling. They are ok at very simple documents, e.g. single column text, centered single level of headings, one image or table per page, etc. (which is what all the MVP demos show). They need another trillion parameters to become bad at complex documents with tables and images.

Right now they hallucinate so badly that you simply _can't_ use them for something as simple as a table with a heading at the top, data in the middle and a summary at the bottom.

I've been hacking away at trying to process PDFs into Markdown, having encountered similar obstacles to OP regarding header detection (and many other issues). OCR is fantastic these days but maintaining a global structure to the document is much trickier. Consistent HTML seems still out of reach for large documents. I'm having half-decent results with Markdown using multiple passes of an LLM to extract document structure and feeding it in contextually for page-by-pass extraction.

The better solution to a search engine extracting text from existing PDFs is to provide advice on how to author PDFs?

What's the timeline for this solution to pay off

That’s true, but it also opens up the vulnerability of the source document being arbitrarily different from the rendered PDF content.

That's true, but it's dependent on the creator of the PDF having aligned incentives with the consumer of the PDF.

In the e-Discovery field, it's commonplace for those providing evidence to dump it into a PDF purely so that it's harder for the opposing side's lawyers to consume. If both sides have lots of money this isn't a barrier, but for example public defenders don't have funds to hire someone (me!) to process the PDFs into a readable format, so realistically they end up taking much longer to process the data, which takes a psychological toll on the defendant. And that's if they process the data at all.

The solution is to make it illegal to do this: wiretap data, for example, should be provided in a standardized machine-readable format. There's no ethical reason for simple technical friction to be affecting the outcomes of criminal proceedings.

Sure, and if you have access to the source document the pdf was generated from, then that is a good thing to do.

But generally speaking, you don't have that control.

I bet 90% of the problem space is legacy PDFs. My company has thousands of these. Some are crappy scans. Some have Adobe's OCR embedded, but most have none at all.

The API in CoreGraphics (MacOS) for PDF, at a basic level, simply presented the text, per page, in the order in which it was encoded in the dictionaries. And 95% of the time this was pretty good — and when working with PDFKit and Preview on the Mac, we got by with it for years.

If you stepped back you could imagine the app that originally had captured/produced the PDF — perhaps a word processor — it was likely rendering the text into the PDF context in some reasonable order from it's own text buffer(s). So even for two columns, you rather expect, and often found, that the text flowed correctly from the left column to the right. The text was therefore already in the correct order within the PDF document.

Now, footers, headers on the page — that would be anyone's guess as to what order the PDF-producing app dumped those into the PDF context.

It can also have embedded binary streams. It was not made for text. It was made for layout and graphics. You give nice examples, but each of those lines could have been broken up into one call per character, or per word, even out of order.

"PDF" is an acronym for for "Portable Document Format"

"2.3.2 Portability

A PDF file is a 7-bit ASCII file, which means PDF files use only the printable subset of the ASCII character set to describe documents even those with images and special characters. As a result, PDF files are extremely portable across diverse hardware and operating system environments."

https://opensource.adobe.com/dc-acrobat-sdk-docs/pdfstandard...

I am hoping at some point to be able to extract tabular data from PDFs for my data wrangling software. If anyone knows of a library that can extract tables from PDFs, can be inegrated into a C++ app and is free or less than a few hundred $, please let me know!

My favorite is (official, governmental) documents that has one set of text that is rendered, and a totally different set of text that you get if you extract the text the normal way..

PDFs inherently are a markup / xml format, the standard is available to learn from.

It's possible to create the same PDF in many, many, many ways.

Some might lean towards exporting a layout containing text and graphics from a graphics suite.

Others might lean towards exporting text and graphics from a word processor, which is words first.

The lens of how the creating app deals with information is often something that has input on how the PDF is output.

If you're looking for an off the shelf utility that is surprisingly decent at pulling structured data from PDFs, tools like cisdem have already solved enough of it for local users. Lots of tools like this out there, many do promise structured data support but it needs to match what you're up to.

I'm tempted to try it. My use case right now is a set of documents which are annual financial and statutory disclosures of a large institution. Every year they are formatted / organized slightly differently which makes it enormously tedious to manually find and compare the same basic section from one year to another, but they are consistent enough to recognize analogous sections from different years due to often reusing verbatim quotes or highly specific key words each time.

What I really want to do is take all these docs and just reorder all the content such that I can look at page n (or section whatever) scrolling down and compare it between different years by scrolling horizontally. Ideally with changes from one year to the next highlighted.

Can your product do this?

Model isn’t rendering the PDF probably, just looking in the file for text.