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Comment by diptanu | Hacker News Reader

Disclaimer - Founder of Tensorlake, we built a Document Parsing API for developers.

This is exactly the reason why Computer Vision approaches for parsing PDFs works so well in the real world. Relying on metadata in files just doesn't scale across different source of PDFs.

We convert PDFs to images, run a layout understanding model on them first, and then apply specialized models like text recognition and table recognition models on them, stitch them back together to get acceptable results for domains where accuracy is table stakes.

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It might sound absurd, but on paper this should be the best way to approach the problem.

My understanding is that PDFs are intended to produce an output that is consumed by humans and not by computers, the format seems to be focused on how to display some data so that a human can (hopefully) easily read them. Here it seems that we are using a technique that mimics the human approach, which would seem to make sense.

It is sad though that in 30+ years we didn't manage to add a consistent way to include a way to make a PDF readable by a machine. I wonder what incentives were missing that didn't make this possible. Does anyone maybe have some insight here?

Probably for the same reason images were not readable by machines.

Except PDFs dangle hope of maybe being machine-readable because they can contain unicode text, while images don't offer this hope.

> the format seems to be focused on how to display some data so that a human can (hopefully) easily read them

It may seem so, but what it really focuses on is how to arrange stuff on a page that has to be printed. Literally everything else, from forms to hyperlinks, were later additions (and it shows, given the crater-size security holes they punched into the format)

Yes, PDFs are primarily a way to describe print data. So to a certain extent the essence of PDF is a hybrid vector-raster image format. Sure, these days text is almost always encoded as or overlaid with actual machine-readable text, but this isn't really necessary and wasn't always done, especially for older PDFs. 15 years ago you couldn't copy (legible) text out of most PDFs made with latex.

1. It's extra work to add an annotation or "internal data format" inside the PDF.

2. By the time the PDF is generated in a real system, the original data source and meaning may be very far off in the data pipeline. It may require incredible cross team and/or cross vendor cooperation.

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

I'm actually much more optimistic of embedding meta data "in-band" with the human readable data, such as a dense QR code or similar.

  > 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.

That is a really interesting idea. Did some napkin math:

Consumer printers can reliably handle 300 Dots Per Inch (DPI). Standard letter paper is 8.5” x 11” and we need a 0.5” margins on all sides to be safe. This gives you a 7.5” x 10” printable area, which is 2250 x 3000 Dots. Assume 1 Dot = 1 QR Code module (cell) and we can pack 432 Version 26 QR codes onto the page (121 modules per side; 4 modules quiet space buffer between them).

A version 26 QR code can store 864 to 1,990 alphanumeric characters depending on error correction level. That’s 373,248 to 859,680 characters per page! Probably need maximum error correction to have any chance of this working.

If we use 4 dots per module, we drop down to 48 Version 18 QR codes (6 x 8). Those can hold 452-1046 alphanumeric characters each, for 20,000 - 50,208 characters per page.

Compare that at around 5000 characters per page of typed English. You can conservatively get 4x the information density with QR codes.

Conclusion: you can add a machine-readable appendix to your text-only PDF file at a cost of increasing page count by about 25%.

Also... many PDFs today are not intended to ever meet a dead tree. If that's the case you can put pretty high DPI QR codes there without issue.

Hmm you could do a bunch of crazy stuff if you assume it will stay digital.

You could have an arbitrarily large page size. You could use color to encode more data… maybe stack QR codes using each channel of a color space (3 for RGB, 4 for CMYK)

There are interesting accessibility and interoperability trade offs. If it’s print-ready with embedded metadata, you can recover the data from a printed page with any smart phone. If it’s a 1 inch by 20 ft digital page of CMKY stacked QR codes, you’ll need some custom code.

Playing “Where’s Waldo” with a huge field of QR codes is probably still way more tractable than handling PDF directly though!

> It might sound absurd, but on paper this should be the best way to approach the problem.

On paper yes, but for electronic documents? ;)

More seriously: PDF supports all the necessary features, like structure tags. You can create a PDF with the basically the same structural information as an HTML document. The problem is that most PDF-generating workflows don’t bother with it, because it requires care and is more work.

And yes, PDF was originally created as an input format for printing. The “portable” in “PDF” refers to the fact that, unlike PostScript files of the time (1980s), they are not tied to a specific printer make or model.

It's Portable Document Format, and the Document refers to paper documents, not computer files.

In other words, this is a way to get a paper document into a computer.

That's why half of them are just images: they were scanned by scanners. Sometimes the images have OCR metadata so you can select text and when you copy and paste it it's wrong.

Kinda funny.

Printing a PDF and scanning it for an email it would normally be worthy of major ridicule.

But you’re basically doing that to parse it.

I get it, have heard of others doing the same. Just seems damn frustrating that such is necessary. The world sure doesn’t parse HTML that way!

Maybe not literally that, but the eldritch horrors of parsing real-world HTML are not to be taken lightly!

I've built document parsing pipelines for a few clients recently, and yeah this approach yields way superior results using what's currently available. Which is completely absurd, but here we are.

Jesus Christ. What other approaches did you try?

I've done only one pipeline trying parse actual PDF structure and the least surprising part of it is that some documents have top-to-bottom layout and others have bottom-to-top, flipped, with text flipped again to be readable. It only goes worse from there. Absurd is correct.

That means you have to put the text (each infividual letter) into its correct place by rendering pdf, but doesnt justify actual OCR which goes one step further and back by rendering and backguessing the glyphs. But thats just text, tables and structure are also somewhere there to be recovered.

If the html in question would include javascript that renders everything, including text, into a canvas -- yes, this is how you would parse it. And PDF is basically that

The analogy doesn't work tho. If you print a PNG and scan it for an email you will be ridiculed. But OCRing a PNG is perfectly valid.

While we have a PDF internals expert here, I'm itching to ask: Why is mupdf-gl so much faster than everything else? (on vanilla desktop linux)

Its search speed on big pdfs is dramatically faster than everything else I've tried and I've often wondered why the others can't be as fast as mupdf-gl.

Thanks for any insights!

It's funny you ask this - i have spent a time building pdf indexing/search apps on the side over the past few weeks.

I'll give you the rundown. The answer to your specific question is basically "some of them process letter by letter to put text back in order, and some don't. Some build fast trie/etc based indexes to do searching, some don't"

All of my machine manuals/etc are in PDF, and too many search apps/OS search indexers don't make it simple to find things in them. I have a really good app on the mac, but basically nothing on windows. All i want is a dumb single window app that can manage pdf collections, search them for words, and display the results for me. Nothing more or less.

So i built one for my non-mac platforms over the past few weeks. One version in C++ (using QT), one version in .net (using MAUI), for fun.

All told, i'm indexing (for this particular example), 2500 pdfs that have about 150k pages in them.

On the indexing side, lucene and sqlite FTS do a fine job, and no issues - both are fast, and indexing/search is not limited by their speed or capability.

On the pdf parsing/text extraction side, i have tried literally every library that i can find for my ecosystem (about 25). Both commercial and not. I did not try libraries that i know share underlying text extraction/etc engines (IE there are a million pdfium wrappers).

I parse in parallel (IE files are processed in parallel) , extract pages in parallel (IE every page is processed in parallel), and index the extracted text either in parallel or in batches (lucene is happy with multiple threads indexing, sqlite would rather have me do it sequentially in batches).

The slowest libraries are 100x slower than the fastest to extract text. They cluster, too, so i assume some of them share underlying strategies or code despite my attempt to identify these ahead of time. The current Foxit SDK can extract about 1000-2000 pages per second, sometimes faster, and things like pdfpig, etc can only do about 10 pages per second.

Pdfium would be as fast as the current foxit sdk but it is not thread safe (I assume this is because it's based on a source drop of foxit from before they added thread safety), so all calls are serialized. Even so it can do about 100-200 pages/second.

Memory usage also varies wildly and is uncorrelated with speed (IE there are fast ones that take tons of memory and slow ones that take tons of memory). For native ones, memory usage seems more related to fragmentation than it does it seems related to dumb things. There are, of course, some dumb things (one library creates a new C++ class instance for every letter)

From what i can tell digging into the code that's available, it's all about the amount of work they do up front when loading the file, and then how much time they take to put the text back in content order to give me.

The slowest are doing letter by letter. The fastest are not.

Rendering is similar - some of them are dominated by stupid shit that you notice instantly with a profiler. For example, one of the .net libraries renders to png encoded bitmaps by default, and between it and windows, it spends 300ms to encode/decode it to display. Which is 10x slower than it rasterized it. If i switch it to render to bmp instead, it takes 5ms to encode/decode it (for dumb reasons, the MAUI apis require streams to create drawable images). The difference is very noticeable if i browse through search results using the up/down key.

Anyway, hopefully this helps answer your question and some related ones.

> From what i can tell digging into the code that's available, ..., how much time they take to put the text back in content order... The slowest are doing letter by letter. The fastest are not.

Thank you, that's really helpful.

I hadn't considered content reordering but it makes perfect sense given that the internal character ordering can be anything, as long as the page renders correctly. There's an interesting comp-sci homework project: Given a document represented by an unordered list of tuples [ (pageNum, x, y, char) ], quickly determine whether the doc contains a given search string.

Sometimes I need to search PDFs for a regex and use pdfgrep. That builds on poppler/xpdf, which extracts text >2x slower than mupdf (https://documentation.help/pymupdf/app1.html#part-2-text-ext..., fitz vs xpdf). From this discussion, I'm now writing my own pdfgrep that builds on mupdf.

So you've outsourced the parsing to whatever software you're using to render the PDF as an image.

Seems like a fairly reasonable decision given all the high quality implementations out there.

How is it reasonable to render the PDF, rasterize it, OCR it, use AI, instead of just using the "quality implementation" to actually get structured data out? Sounds like "I don't know programming, so I will just use AI".

PDFs don't always lay out characters in sequence, sometimes they have absolutely positioned individual characters instead.

PDFs don't always use UTF-8, sometimes they assign random-seeming numbers to individual glyphs (this is common if unused glyphs are stripped from an embedded font, for example)

etc etc

But all those problems exist when rendering into a surface or rastering. I just don't understand how one thinks, this is a hard problem, let me make it harder by solving the problem into another kind of problem that is just as hard as solving it in the first place (PDF to structured data vs PDF to raster). And then solve the new problem, which is also hard. It is absurd.

Sometimes scanned documents are structured really weird, especially for tables. Visually, we can recognize the intention when it's rendered, and so can the AI, but you practically have to render it to recover the spatial context.

But why do you have to render it into bitmap?

I don’t think people are suggesting : Build a renderer > build an ocr pipeline > run it on pdfs

I think people are suggesting : Use a readymade renderer > use readymade OCR pipelines/apis > run it on pdfs

A colleague uses a document scanner to create a pdf of a document and sends it to you

You must return the data represented in it retaining as much structure as possible

How would you proceed? Return just the metadata of when the scan was made and how?

Genuinely wondering

You can use an existing readymade renderer to render into structured data instead of raster.

Just to illustrate this point, poppler [1] (which is the most popular pdf renderer in open source) has a little tool called pdf2cairo [2] which can render a pdf into a svg. This means you can delegate all pdf rendering to poppler and only work with actual graphical objects to extract semantics.

I think the reason this method is not popular is that there are still many ways to encode a semantic object graphically. A sentence can be broken down into words or letters. Table lines can be formed from multiple smaller lines, etc. But, as mentioned by the parent, rule based systems works reasonably well for reasonably focused problems. But you will never have a general purpose extractor since rules needs to be written by humans.

[1] https://poppler.freedesktop.org/ [2] https://gitlab.freedesktop.org/poppler/poppler/-/blob/master...

There is also PDF to HTML, PDF to Text, MuPDF also has PDF to XML, both projects along with a bucketful of other PDF toolkits have PDF to PS, and there is many many XML, HTML, and Text outputs for PS.

Rastering and OCR'ing PDF is like using regex to parse XHTML. My eyes are starting to bleed out, I am done here.

It looks like you make a lot of valid points, but also have an extremely visceral reaction because theres a company out there thats using AI in a way that offends you. I mean fair still.

But im a guy who's in the market for a pdf parser service, im happy to pay pretty penny per page processed. I just want a service that works without me thinking for a second about any of the problems you guys are all discussing. What service do I use? Do I care if it uses AI in the lamest way possible? The only thing that matters are the results. There are two people including you in this thread ramming with pdf parsing gyan but from reading it all, it doesn't look like I can do things the right way without spending months fully immersed in this problem alone. If you or anyone has a non blunt AI service that I can use Ill be glad to check it out.

It is a hard problem, yes, but you don't solve it by rastering it, OCR, and then using AI. You render it into a structured format. Then at least you don't have to worry about hallucinations, fancy fonts OCR problems, text shaping problems, huge waste of GPU and CPU to paint an image only to OCR it and throw it away.

Use a solution that renders PDF into structured data if you want correct and reliable data.

pdftotext from poppler has that without doing juggling with formats.

PDF to raster seems a lot easier than PDF to structured data, at least in terms of dealing with the odd edge cases. PDF is designed to raster consistently, and if someone generates something that doesn't raster in enough viewers, they'll fix it. PDF does not have anything that constrains generators to a sensible structured representation of the information in the document, and most people generating PDF documents are going to look at the output, not run it through a system to extract the structured data.

The problems don't actually exist in the way you think.

When extracting text directly, the goal is to put it back into content order, regardless of stream order. Then turn that into a string. As fast as possible.

That's straight text. if you want layout info, it does more. But it's also not just processing it as a straight stream and rasterizing the result. It's trying to avoid doing that work.

This is non-trivial on lots of pdfs, and a source of lots of parsing issues/errors because it's not just processing it all and rasterizing it, but trying to avoid doing that.

When rasterizing, you don't care about any of this at all. PDFs were made to raster easily. It does not matter what order the text is in the file, or where the tables are, because if you parse it straight through, raster, and splat it to the screen, it will be in the proper display order and look right.

So if you splat it onto the screen, and then extract it, it will be in the proper content/display order for you. Same is true of the tables, etc.

So the direct extraction problems don't exist if you can parse the screen into whatever you want, with 100% accuracy (and of course it doesn't matter if you use AI or not to do it).

Now, i am not sure i would use this method anyway, but your claim that the same problems exist is definitely wrong.

> Sounds like "I don't know programming, so I will just use AI".

If you were leading Tensorlake, running on early stage VC with only 10 employees (https://pitchbook.com/profiles/company/594250-75), you'd focus all your resources on shipping products quickly, iterating over unseen customer needs that could make the business skyrocket, and making your customers so happy that they tell everyone and buy lots more licenses.

Because you're a stellar tech leader and strategist, you wouldn't waste a penny reinventing low-level plumbing that's available off-the-shelf, either cheaply or as free OSS. You'd be thinking about the inevitable opportunity costs: If I build X then I can't build Y, simply because a tiny startup doesn't have enough resources to build X and Y. You'd quickly conclude that building a homegrown, robust PDF parser would be an open-ended tar pit that precludes us from focusing on making our customers happy and growing the business.

And the rest of us would watch in awe, seeing truly great tech leadership at work, making it all look easy.

I would hire someone who understands PDFs instead of doing the equivalent of printing a digital document and scanning it for "digital record keeping". Stop everything and hire someone who understands the basics of data processing and some PDF.

What's the economic justification?

Let's assume we have a staff of 10 and they're fully allocated to committed features and deadlines, so they can't be shifted elsewhere. You're the CTO and you ask the BOD for another $150k/y (fully burdened) + equity to hire a new developer with PDF skills.

The COB asks you directly: "You can get a battle-tested PDF parser off-the-shelf for little or no cost. We're not in the PDF parser business, and we know that building a robust PDF parser is an open-ended project, because real-world PDFs are so gross inside. Why are you asking for new money to build our own PDF parser? What's your economic argument?"

And the killer question comes next: "Why aren't you spending that $150k/y on building functionality that our customers need?" If don't give a convincing business justification, you're shoved out the door because, as a CTO, your job is building technology that satisfies the business objectives.

So CTO, what's your economic answer?

The mistake all of you're making is the assumption that PDF rendering means rasteration. Everything else crumbles down from that misconception.

So if you receive a pdf full of sections containing prerasterized text (e.g adverts, 3d rendered text with image effects, scanned documents, handwritten errata), what do you do? You cannot use OCR because apparently only pdf-illiterate idiots would try such a thing?

I wouldn't start by rastering the rest of the PDF. In business world, unlike academia and bootleg books and file sharing, majority of PDFs are computer generated. I know because I do this for a living.

I think it's reasonable because their models are probably trained on images, and not whatever "structured data" you may get out of a PDF.

Yes this! We training it on a ton of diverse document images to learn reading order and layouts of documents :)

But you have to render the PDF to get an image, right? How do you go from PDF to raster?

No model can do better on images than structured data. I am not sure if I am on crack or you're all talking nonsense.

You are assuming structure where there is none. It's not the crack, it's the lack of experience with PDF from diverse sources. Just for instance, I had a period where I was _regularly_ working with PDF files with the letters in reverse order, each letter laid out individually (not a single complete word in the file).

You're thinking "rendering structured data" means parsing PDF as text. That is just wrong. Carefully read what I said. You render the PDF, but into structured data rather than raster. If you still get letters in reverse when you render your PDF into structured data, your rendering engine is broken.

How do you render into structured data, from disparate letters that are not structured?

  D10
  E1
  H0
  L2,3,9
  O4,7
  R8
  W6

I'm sure that you could look at that and figure out how to structure it. But I highly doubt that you have a general-purpose computer program that can parse that into structured data, having never encountered such a format before. Yet, that is how many real-world PDF files are composed.

It is called rendering. MuPDF, Poppler, PDFjs, and so on. The problem is that you and everyone else thinks "rendering" means bitmaps. That is not how it works.

Then I would very much appreciate if you would enlighten me. I'm serious, I would love nothing more than for you to prove your point, teach me something, and win an internet argument. Once rendered, do any of the rendering engines have e.g. a selectable or accessible text? Poppler didn't, neither did some Java library that I tried.

For me, learning something new is very much worth losing the internet argument!

I have explained the details in other comments, have a look. But you can start by looking at pdftotext from Poppler, it is ready to go for 60-70% of cases with -layout flag, with bbox-layout you get even more details.

Thank you. Even with box layout one can not even know that there is a coherent word or phrase to extract, without visually inspecting the PDF beforehand. I've been there, fighting with it right in the CLI and finding that there is no way to even progress to a script.

The advantage of the OCR method is that it effectively performs that visual inspection. That's why it is preferable for PDFs of disparate origin.

What kind of semantics can you infer from the text of OCRing a bitmap that you can't infer from the text generated directly from the PDF? Is it the lack of OCR mistakes? The hallucinations? Or something else?

In the cases that I've seen, the PDF software does not generate text strings. It generates individual letters. It is up to any application to try to figure out how those individual letters relate to one another.

Did you even read my comment? The "application" is called pdftotext, and instead of putting the individual letters on a bitmap, it puts them in a string literal.

I do not understand why you insist on being polemic to win an internet argument, when I'm giving you all the tools to win the internet argument by virtue of being correct.

I did read your comment, because my intention here is to learn. I already described how tools such as pdftotext do not produce strings when each letter is positioned independently. I even gave an example of a few replies up.

As someone who had to parse form data from a pdf, where the pdf author named the inputs TextField1 TextField2 TextFueld3 etc.

Misspellings, default names, a mixture, home brew naming schemes, meticulous schemes, I’ve seen it all. It’s definitely easier to just rasterize it and OCR it.

Same. Then someone edits the form and changes the names of several inputs, obsoleting much of the previous work, some of which still needs to be maintained because multiple versions are floating around.

I do PDF for a living, millions of PDFs per month, this is complete nonsense. There is no way you get better results from rastering and OCR than rendering into XML or other structured data.

How many different PDF generators have done those millions of PDFs tho?

Because you're right if you're paid to evaluate all the formats with the Mark 1 eyeball and do a custom parser for each. It sounds like it's feasible for your application.

If you want a generic solution that doesn't rely on a human spending a week figuring out that those 4 absolutely positioned text fields are the invoice number together (and in order 1 4 2 3), maybe you're wrong.

Source: I don't parse pdfs for a living, but sometimes I have to select text out of pdf schematics. A lot of times I just give up and type what my Mark 1 eyeball sees in a text editor.

We process invoices from around the world, so more PDF generators than I care to count. It is hard a problem for sure, but the problem is the rendering, you can't escape that by rastering it, that is rendering.

So it is absurd to pretend you can solve the rendering problem by rendering it into an image instead of a structured format. By rendering it into a raster, now you have 3 problems, parsing the PDF, rendering quality raster, then OCR'ing the raster. It is mind numbingly absurd.

Rendering is a different problem from understanding what's rendered.

If your PDF renders a part of the sentence at the beginning of the document, a part in the middle, and a part at the end, split between multiple sections, it's still rather trivial to render.

To parse and understand that this is the same sentence? A completely different matter.

Computers "don't understand" things. They process things, and what you're saying is called layoutinng which is a key part of PDF rendering. I do understand for someone unfamiliar with the internals of file formats, parsing, text shapping, and rendering in general, it all might seem like a blackmagic.

No one said it was as black magic. In the context of OCR and parsing PDFs to convert them to structured data and/or text, rendering is a completely different task from text extraction.

As people have pointed out many times in the discussion: https://news.ycombinator.com/item?id=44783004, https://news.ycombinator.com/item?id=44782930, https://news.ycombinator.com/item?id=44789733 etc.

You're wrong. There is nothing inherent in "rendering" that means "raster or pixels". You can render PDFs or any format into any format you want, including XML for example.

In fact, in majority of PDFs, a large part of rendering has to do with composing text.

You are using the Mark 1 eyeball for each new type of invoice to figure out what field goes where, right?

It is a bit more involved, we have a rule engine that is fine tuned over time and works on most of invoices, there is also an experimental AI based engine that we are running in parallel but the rule based Engine still wins on old invoices.

I sort of agree... I do the same.

We also parse millions of PDFs per month in all kinds languages (both Western and Asian alphabets).

Getting the basics of PDF parsing to work is really not that complicated -- A few months work. And is an order of magnitude more efficient than generating an image in 300-600 DPI and doing OCR or Visual LLM.

But some of the challenges (which we have solved) are:

• Glyphs to unicode tables are often limited or incorrect • "Boxing" blocks of text into "paragraphs" can be tricky • Handling extra spaces and missing spaces between letters and words. Often PDFs do not include the spaces or they are incorrect so you need to identify gaps yourself. • Often graphic designers of magazines/newspapers will hide text behind e.g. a simple white rectangle, and place new version of the text above. So you need to keep track of z-order and ignore hidden text. • Common text can be embedded as vector paths -- Not just logos but we also see it with text. So you need a way to handle that. • Dropcap and similar "artistic" choices can be a bit painful

There are lot of other smaller issues -- but they are generally edge cases.

OCR handles some of these issues for you. But we found that OCR often misidentifies letters (all major OCR), and they are certainly not perfect with spaces either. So if you are going for quality, you can get better results if you parse the PDFs.

Visual Transformers are not good with accurate coordinates/boxing yet -- At least we haven't seen a good enough implementation of it yet. Even though it is getting better.

We tried the xml structured route, only to end up with pea soup afterwards. Rasterizing and OCR was the only way to get standardized output.

I know OCR is easier to set up, but you lose a lot going that way.

We process several million pages from Newspapers and Magazines from all over the world with medium to very high complexity layouts.

We built the PDF parser on top of open source PDF libraries, and this gives many advantages: • We can accurately get headlines other text placed on top on images. OCR is generally hopeless with text placed on top of images or on complex backgrounds • Distinguish letters accurately (i.e. number 1, I, l, "o", "zero") • OCR will pick up ghost letters from images, where OCR program believes there is text, even if there isn't. We don't. • We have much higher accuracy than OCR because we don't depend on the OCR programs' ability to recognize the letters. • We can utilize font information and accurate color information, which helps us distinguish elements from each other. • We have accurate bounding box locations of each letter, word, line, and block (pts).

To do it, we completely abandon the PDF text-structure and only use the individual location of each letter. Then we combine letter positions to words, words to lines, and lines to text-blocks using a number of algorithms.

We use the structure blocks that we generated with machine learning afterwards, so this is just the first step in analyzing the page.

It may seem like a large undertaking, but it literally only took a few months to built this initially, and we have very rarely touched the code over the last 10 years. So it was a very good investment for us.

Obviously, you can achieve a lot of the same with OCR -- But you lose information, accuracy, and computational efficiency. And you depend on the OCR program you use. Best OCR programs are commercial and somewhat pricy at scale.

Do you have your parser released as a service? Curious to test it out.

We were 99.99% accurate with our OCR method. It’s not just vanilla ocr but a couple of extractions of metadata (including the xml from the forms) and textract-like json of the document to perform ocr on the right parts.

A lot has changed in 10 years. This was for a major financial institution and it worked great.

> To do it, we completely abandon the PDF text-structure and only use the individual location of each letter. Then we combine letter positions to words, words to lines, and lines to text-blocks using a number of algorithms. We use the structure blocks that we generated with machine learning afterwards, so this is just the first step in analyzing the page.

Do you happen to have any sources for learning more about the piecing together process? E.g. the overal process and the algorithms involved etc. It sounds like an interesting problem to solve.

> How is it reasonable to render the PDF, rasterize it, OCR it, use AI, instead of just using the "quality implementation" to actually get structured data out?

Because the underlying "structured data" is never checked while the visual output is checked by dozens of people.

"Truth" is the stuff that the meatbags call "truth" as seen by their squishy ocular balls--what the computer sees doesn't matter.

Your mistake is in thinking that computers "see the image", second, you somehow think the output of OCR is different from a PDF engine that renders it into structured data/text.

There are many cases images are exported as PDFs. Think invoices or financial statements that people send to financial services companies. Using layout understanding and OCR based techniques leads to way better results than writing a parser which relies on the files metadata.

The other thing is segmenting a document and linearizing it so that an LLM can understand the content better. Layout understanding helps with figuring out the natural reading order of various blocks of the page.

  > There are many cases images are exported as PDFs.

One client of a client would print out her documents, then "scan" them with an Android app (actually just a photograph wrapped in a PDF). She was taught that this application is the way to create PDF files, and would staunchly not be retrained. She came up with this print-then-photograph after being told not to photograph the computer monitor - that's the furthest retraining she was able to absorb.

Be there no mistake, this woman was extremely successful at her field. Successful enough to be a client of my client. But she was taught that PDF equals that specific app, and wasn't going to change her workflow to accommodate others.

> How is it reasonable to render the PDF, rasterize it, OCR it, use AI, instead of just using the "quality implementation" to actually get structured data out?

Because PDFs might not have the data in a structured form; how would you get the structured data out of an image in the PDF?

Sir, some of our cars breaks down every now and then, so we push them, because it happens every so often and we want to avoid it, we have implemented a policy of pushing all cars instead of driving them at all times. This removes the problem of pushing cars.

> instead of just using the "quality implementation" to actually get structured data out?

I suggest spending a few minutes using a PDF editor program with some real-world PDFs, or even just copying and pasting text from a range of different PDFs. These files are made up of cute-tricks and hacks that whatever produced them used to make something that visually works. The high-quality implementations just put the pixels where they're told to. The underlying "structured data" is a lie.

EDIT: I see from further down the thread that your experience of PDFs comes from programmatically generated invoice templates, which may explain why you think this way.

We do a lot of parsing of PDFs and basically break the structure into 'letter with font at position (box)' because the "structure" within the PDF is unreliable.

We have algorithms that combines the individual letters to words, words to lines, lines to boxes all by looking at it geometrically. Obviously identify the spaces between words.

We handle hidden text and problematic glyph-to-unicode tables.

The output is similar to OCR except we don't do the rasterization and quality is higher because we don't depend on vision based text recognition.

The base implementation of all this, I made in less than a month 10 years ago and we rarely, if ever, touch it.

We do machine learning afterwards on the structure output too.

Very interesting. How often do you encounter PDFs that are just scanned pages? I had to make heavy use of pdfsandwich last time I was accessing journal articles.

> quality is higher because we don't depend on vision based text recognition

This surprises me a bit; outside of an actual scan leaving the computer I’d expect PDF->image->text in a computer to be essentially lossless.

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.

  > just using the "quality implementation"?

What is the quality implementation?

I just spent a few weeks testing about 25 different pdf engines to parse files and extract text.

Only three of them can process all 2500 files i tried (which are just machine manuals from major manufacaturers, so not highly weird shit) without hitting errors, let alone producing correct results.

About 10 of them have a 5% or less failure rate on parsing the files (let alone extracting text). This is horrible.

It then goes very downhill.

I'm retired, so i have time to fuck around like this. But going into it, there is no way i would have expected these results, or had time to figure out which 3 libraries could actually be used.

PDF is a list of drawing commands (not exactly but a useful simplification). All those draw commands from some JS lib or in SVG? Or in every other platform's API? PDF or Postscript probably did them first. The model of "there is some canvas in which I define coordinate spaces then issue commands to draw $thing at position $(x,y), scaled by $z".

You might think of your post as a <div>. Some kind of paragraph or box of text in which the text is laid out and styles applied. That's how HTML does it.

PDF doesn't necessarily work that way. Different lines, words, or letters can be in entirely different places in the document. Anything that resembles a separator, table, etc can also be anywhere in the document and might be output as a bunch of separate lines disconnected from both each other and the text. A renderer might output two-column text as it runs horizontally across the page so when you "parse" it by machine the text from both columns gets interleaved. Or it might output the columns separately.

You can see a user-visible side-effect of this when PDF text selection is done the straightforward way: sometimes you have no problem selecting text. In other documents selection seems to jump around or select abject nonsense unrelated to cursor position. That's because the underlying objects are not laid out in a display "flow" the way HTML does by default so selection is selecting the next object in the document rather than the next object by visual position.

Because PDF is as much a vector graphics format as a document format, you cannot expect the data to be reasonably structured. For example applications can convert text to vector outlines or bitmaps for practical or artistic purposes (anyone who ever had to deal with transparency "flattening" issues knows the pain), ideally they also encode the text in a seperate semantic representation. But many times PDF files are exported from "image centric" programs with image centric workflows (e.g. Illustrator, CorelDraw, Indesign, QuarkXpress, etc) where the main issue being solved for is presentational content, not semantic. For example if I receive a Word document and need to layout it so it fits into my multi column magazine layout I will take the source text and break it into seperate sections which then manually get copy and pasted into InDesign. You can import the document directly but for all kinds of practical reasons this is not the default way of working. Some asides and lists might be broken out of the original flow of text and placed in their own text field, etc. So now you lost the original semantic structure. Remember, this is how desktop publishing evolved: for print, which has no notion of structure or metadata embedded into the ink or paper. Another common usecase is to simply have resolution independent graphics, again, display purposes only, no structured data is required nor expected.

This has close to zero relevance to the OP.

It's a good ad tho

I think it's a useful insight for people working on RAG using LLMs.

Devs working on RAG have to decide between parsing PDFs or using computer vision or both.

The author of the blog works on PdfPig, a framework to parse PDFs. For its document understanding APIs, it uses a hybrid approach that combines basic image understanding algorithms with PDF metadata . https://github.com/UglyToad/PdfPig/wiki/Document-Layout-Anal...

GP's comment says a pure computer vision approach may be more effective in many real-world scenarios. It's an interesting insight since many devs would assume that pure computer vision is probably the less capable but also more complex approach.

As for the other comments that suggest directly using a parsing library's rendering APIs instead of rasterizing the end result, the reason is that detecting high-level visual objects (like tables , headings, and illustrations) and getting their coordinates is far easier using vision models than trying to infer those structures by examining hundreds of PDF line, text, glyph, and other low-level PDF objects. I feel those commentators have never tried to extract high-level structures from PDF object models. Try it once using PdfBox, Fitz, etc. to understand the difficulty. PDF really is a terrible format!

> This is exactly the reason why Computer Vision approaches for parsing PDFs works so well in the real world.

One of the biggest benefits of PDFs though is that they can contain invisible data. E.g. the spec allows me to embed cryptographic proof that I've worked at the companies I claim to have worked at within my resume. But a vision-based approach obviously isn't going to be able to capture that.

Cryptographic proof of job experience? Please explain more. Sounds interesting.

If someone told me there was cryptographic proof of job experience in their PDF, I would probably just believe them because it’d be a weird thing to lie about.

Encrypted (and hidden) embedded information, e. g. documents, signatures, certificates, watermarks, and the like. To (legally-binding) standards, e. g. for notary, et cetera.

In theory your (old) boss could sign part of your CV with a certificate obtained from any CA participating in Adobe's AATL programme. If you use the software right, you could have different ranges signed by different people/companies. Because only a small component gets signed, you'd need them to sign text saying "Jane Doe worked at X corp and did their job well" as a signed line like "software developer" can be yanked out and placed into other PDF documents (simplifying a little here).

I'm not sure if there's software out there to make that process easy, but the format allows for it. The format also allows for someone to produce and sign one version and someone else to adjust that version and sign the new changes.

Funnily enough, the PDF signature actually has a field to refer to a (picture of) a readable signature in the file, so software can jot down a scan of a signature that automatically inserts cryptographic proof.

In practice I've never seen PDFs signed with more than one signature. PDF readers from anyone but Adobe seem to completely ignore signatures unless you manually open the document properties, but Adobe Reader will show you a banner saying "document signed by XYZ" when you open a signed document.

What software can be used to write and read this invisible data? I want to document continuous edits to published documents which cannot show these edits until they are reviewed, compiled and revised. I was looking at doing this in word, but we keep word and PDF versions of these documents.

Yeah we don't handle this yet.

If that stuff is stored as structured metadata extracting that should be trivial

Nutrient.io Co-Founder here: We’ve been doing PDF for over 10y. PDF Viewers like Web browsers have to be liberal in what they accept, because PDF has been around for so long, and like with HTML ppl generating files often just iterate until they have something that displays correctly in the one viewer they are testing with.

That’s why we built our AI Document Processing SDK (for PDF files) - basically a REST API service, PDF in, structured data in JSON out. With the experience we have in pre-/post-processing all kinds of PDF files on a structural not just visual basis, we can beat purely vision based approaches on cost/performance: https://www.nutrient.io/sdk/ai-document-processing

If you don’t want to suffer the pain of having to deal with figuring this out yourself and instead focus on your actual use case, that’s where we come in.

Looks super interesting, except for there's no pricing on the page that I could find except for contact sales - totally understand wanting to do a higher touch sales process, but that's going to bounce some % of eng types who want to try things out but have been bamboozled before.

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.

I love that the employee’s (CEO’s?) response to a “there’s no pricing on your website” comment is a link to a review on another kinda random website of a testimonial that getting pricing from them sucks and was marginally above the baseline of “the customer didn’t get scammed.” Ringing endorsement, along with the implied “we’ve been doing this ten years and still haven’t been able to implement self service sign up or even an html pricing page on the site.”

You realise that testimonial is saying your pricing policy sucks, but after wasting their time on sales calls with you, trusted you more with it than the also sucky competition?

I think youre losing a customer because you don't have that option. Im not gonna contact sales and sit through another inane sales pitch zoom call that should be no more than 5 minutes stretched to an hour before I even know if your solution works. And im most definitely not gonna keep fingers crossed the pricing makes sense.

This is the parallel of some of the dotcom peak absurdities. We are in the AI peak now.

> "This is exactly the reason why Computer Vision approaches for parsing PDFs works so well in the real world."

Well, to be fair, in many cases there's no way around it anyway since the documents in question are only scanned images. And the hardest problems I've seen there are narrative typography artbooks, department store catalogs with complex text and photo blending, as well as old city maps.

I have started treading everything as images when multimodal LLMs appeared. Even emails. It's so much more robust. Especially emails are often used as a container to send a PDF (e.g. a contract) that contains an image of a contract that was printed. Very very common.

I have just moved my company's RAG indexing to images and multimodal embedding. Works pretty well.

I would like to add the ability to import data tables from PDF documents to my data wrangling software (Easy Data Transform). But I have no intention of coding it myself. Does anyone know of a good library for this? Needs to be:

-callable from C++

-available for Windows and Mac

-free or reasonable 1-time fee

I was wondering : is your method ultimately, produces a better parsing than the program you used to initially parse and display the pdf? Or is the value in unifying the parsing for different input parsers?

Doesn't rendering to an image require proper parsing of the PDF?

PDF is more like a glorified svg format than a word format.

It only contains info on how the document should look but so semantic information like sentences, paragraphs, etc. Just a bag of characters positioned in certain places.

Sometimes the characters aren’t even characters, just paths

Wouldn't that be very space inefficient to repeat the paths every time a letter appears in the file? Or you mean that glyph Ids don't necessarily map to Unicode?

sometimes in fancy articles you might see the first letter is large and ornate which is most likely a path also like you said glyph IDs always don't necessarily map to unicode or the creator can intentionally mangle the 'to unicode' map of Identity-H embedded font in the pdf if he is nasty

Outlines are just a practical way of handling less common display cases.

Just to give a practical example. Imagine a Star Wars advert that has the Star Wars logo at the top, specified in outlines because that's what every vector logo uses. Below it the typical Star Wars intro text stretched into perspective, also using outlines, because that's the easiest (display engine doesn't need complicated transformation stack), efficient to render (you have to render the outlines anyway), and most robust way (looks the same everywhere), way of implementing transformations in text. You also don't have to supply the font file, which comes with licensing issues, etc. Also whenever compositing and transparency are involved, with color space conversion nonsense, it's more robust to "bake" the effect via constructive geometry operations, etc, to prevent display issues on other devices, which are surprisingly common.

Yes, and don't for a second think this approach of rastering and OCR'ing is sane, let alone a reasonable choice. It is outright absurd.

Noone has claimed getting structured data out of pdfs are sane. What you seem to be missing is that there are no sane ways to get a decent output. The reasonable choice would be to not even try, but business needs invalidate that choice. So what remain is the absurd ways to solve the problem.

It sounds like a trap coyote would use to catch roadrunner. Does it really have to be so convoluted?

I do same but for document search. Colqwen + a VLM like claude.

Thanks for the pointer!

How ridiculous.

`mutool convert -o <some-txt-file-name.txt> -F text <somefile.pdf>`

Disclaimer: I work at a company that generates and works with PDFs.

So you parse PDFs, but also OCR images, to somehow get better results?

Do you know you could just use the parsing engine that renders the PDF to get the output? I mean, why raster it, OCR it, and then use AI? Sounds creating a problem to use AI to solve it.

Yes, but a lot of the improvement is coming from layout models and/or multimodal LLMs operating directly on the raster images, as opposed to via classical OCR. This gets better results because the PDF format does not necessarily impart reading order or semantic meaning; the only way to be confident you're reading it like a human would is to actually do so - to render it out.

Another thing is that most document parsing tasks are going to run into a significant volume of PDFs which are actually just a bunch of scans/images of paper, so you need to build this capability anyways.

TL;DR: PDFs are basically steganography

Hard no.

LLMs aren't going to magically do more than what your PDF rendering engine does, rastering it and OCR'ing doesn't change anything. I am amazed at how many people actually think it is a sane idea.

I think there is some kind of misunderstanding. Sure, if you get somehow structured, machine-generated PDFs parsing them might be feasible.

But what about the "scanned" document part? How do you handle that? Your PDF rendering engine probably just says: image at pos x,y with size height,width.

So as parent says you have to OCR/AI that photo anyway and it seems that's also a feasible approach for "real" pdfs.

Okay, this sounds like "because some part of the road is rough, why don't we just drive in the ditch along the road way all the way, we could drive a tank, that would solve it"?

My experience is that “text is actually images or paths” is closer to the 40% case than the 1% case.

So you could build an approach that works for the 60% case, is more complex to build, and produces inferior results, but then you still need to also build the ocr pipeline for the other 40%. And if you’re building the ocr pipeline anyway and it produces better results, why would you not use it 100%?

We parse PDFs to convert them to text in a linearized fashion. The use case for this would be to use the content for downstream use cases - search engine, structured extraction, etc.

None of that changes the fact that to get a raster, you have to solve the PDF parsing/rendering problem anyways, so might as well get structured data out instead of pixels so that it now another problem (OCR).

Well, you clearly hasn't parsed a wide variety of pdfs. Because if you had, you had been exposed to pdfs that contain only images, or those that contain embedded text, but that embedded text is utter nonsense and doesn't match what is shown on the page when rendered.

And that is before we even get into text structure, because as everyone knows, reading text is easier if things like paragraphs, columns and tables are preserved in the output. And guess what, if you just use the parsing engine for that, then what you get out is a garbled mess.

If your rendering engine doesn't output what is shown, your engine is broken, and it can be broken whatever you render it into bitmap or structured data.

While you're doing this, please also tell people to stop producing PDF files in the first place, so that eventually the number of new PDFs can drop to 0. There's no hope for the format ever since manager types decided that it is "a way to put paper in the computer" and not the publishing intermediate format it was actually supposed to be. A vague facsimile of digitization that should have never taken off the way it did.

PDFs serve their purpose well. Except for some niche open source Linux tools, they render the same way in every application you open them in, in practically every version of that application. Unlike document formats like docx/odf/tex/whatever files that reformat themselves depending on the mood of the computer on the day you open them. And unlike raw image files, you can actually comfortably zoom in and read the text.

You don't need the exact flowing of text to be consistent, outside of publishing. This is an anti-feature most of the time, something you specifically don't want.

Zooming is not something PDFs do well at all. I'm not sure in what universe you could call this a usability benefit. Just because it's made of vector graphics doesn't mean you've implemented zoom in a way that is actually usable. People with poor vision (who cannot otherwise use eyeglasses) don't use a magnifying glass, they use the large-print variant of a document. Telling them to use a magnifying glass would be saying "no, we did not accommodate for low eyesight at all, deal with it".

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