So you want to parse a PDF?

  > Chicken and egg. There are very few if any machine parseable PDFs out there, so there is little demand for such.

No, the egg has been laid for quite some time. There's just not enough chicken. Almost every place I've worked at has complained about the parsability of PDF files until I showed them LibreOffice's PDF export feature, that supports PDF/A (arciveable), PDF/UA (Universal Accessibility), and embedding the original .odt file in the PDF itself. That combo format has saved so many people so much headache, I don't know why it is not more widely known.

This happens -- also variants which have been processed with OCR.

So if it is scanned it contains just a single image - no text.

OCR programs will commonly create a PDF where the text/background and detected images are separate. And then the OCR program inserts transparent (no-draw) letters in place of the text it has identified, or (less frequently) place the letters behind the scanned image in the PDF (i.e. with lower z).

We can detect if something has been generated by an OCR program by looking at the "Creator data" in the PDF that describes the program use to create the PDF. So we can handle that differently (and we do handle that a little bit differently).

PDF->image->text is 100% not lossless.

When you rasterize the PDF, you losing information because you are going from a resolution independent format to a specific resolution: • Text must be rasterized into letters at the target resolution • Images must be resampled at the target resolution • Vector paths must be rasterized to the target resolution

So for example the target resolution must be high enough that small text is legible.

If you perform OCR, you depend on the ability of the OCR program to accurately identify the letters based on the rasterized form.

OCR is not 100% accurate, because it is computer vision recognition problem, and • there are hundrends of thousands of fonts in the wild each with different details and appearances. • two letters can look the same; simple example where trivial OCR/recognition fails is capital letter "I" and lower case "l". These are both vertical lines, so you need the context (letters nearby). Same with "O" and zero. • OCR is also pretty hopeless with e.g. headlines/text written on top of images because it is hard to distinguish letters from the background. But even regular black on white text fails sometimes. • OCR will also commonly identify "ghost" letters in images that are not really there. I.e. pick up a bunch of pixels that have been detected as a letter, but really is just some pixel structure part of the image (not even necessarily text on the image) -- A form of hallucination.

I've been pondering for a while that we need to move away from layout-based written communication. As in, the need to make things look professionally laid out is an anachronism, and is (very) rarely related to comprehension of the actual content.

For example, submissions to regulatory agencies are huge documents; we spend lots of time in (typically) Microsoft Word creating documents that follow a layout tradition. Aside from this time spent (wasted), the downside is that to guarantee that layout for the recipient, the file must be submitted in DOCX or PDF. These formats are then unfriendly if you want to do anything programatically with them, extract raw data, etc. And of course, while LLMs can read such files, there's likely a significant computational overhead vs. a file in a simple machine-readable format (e.g. text, markdown, XML, JSON).

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An alternative approach would be to adopt a very simple 'machine first', or 'content first' format - for example, based on JSON, XML, even HTML - with minimum metadata to support strurcture, intra-document links, and embedding of images. For human comsumption, a simple viewer app would reconstitute the file into something more readable; for machine consumption, the content is already directly available. I'm well aware that such formats already exist - HTML/browsers, or EPUB/readers, for example - the issue is to take the rational step towards adopting such a format in place of the legacy alternatives.

I'm hoping that the LLM revolutoion will drive us in just this direction, and that in time, expensive parsing of PDFs is a thing of the past.

I know - we're working on adding Self-Serve sign up for non-enterprise deals.

But regarding our pricing - I can point you at an actual testimonial https://www.g2.com/products/pspdfkit-sdk/reviews/pspdfkit-sd...

> These pricing structures can be complex and NEED to be understood fully before moving forward with purchase. However, out of all of the solutions that I reviewed, [Nutrient] was the one that walked me through their pricing the best and didn't make me feel like I was going to get fleeced.

Well, perhaps you are exposed only to special snowflakes of pdfs that are from a single source and somewhat well formed and easy to extract from. Other, like me, are working at companies that also have lots of PDFs, from many, many different sources, and there are no easy ways to extract structured data or even text in a way that always work.