41–50 of 206 posts
Cloudflare’s ai.toMarkdown() function available in Workers AI can handle PDFs pretty easily. Judging from speed alone, it seems they’re parsing the actual content rather than shoving into OCR/LLM. Shameless plug: I use this under the hood when you prefix any PDF URL with https://pure.md/ to convert to raw text.
I think using Gemma3 in vision mode could be a good use-case for converting PDF to text. It’s downloadable and runnable on a local computer, with decent memory requirements depending on which size you pick. Did anyone try it?
So many of these problems have been solved by mozilla pdf.js together with its viewer implementation: https://mozilla.github.io/pdf.js/ .
The PDF itself is still flawed, even if pdf.js interprets it perfectly, which is still a problem for non-pdf.js viewers and tasks where "viewing" isn't the primary goal.
The better solution is to embed, in the PDF, the editable source document. This is easily done by LibreOffice. Embedding it takes very little space in general (because it compresses well), and then you have MUCH better information on what the text is and its meaning. It works just fine with existing PDF readers.
So many of these problems have been solved by mozilla pdf.js together with its viewer implementation: https://mozilla.github.io/pdf.js/ .
I don't think so, pdf.js is able to render a pdf content. Which is different from extracting "text". Text in PDF can be encoded in many ways, in an actual image, in shapes (think, segments, quadratic bezier curves...), or in an XML format (really easy to process). PDF viewers are able to render text, like a printer would work, processing command to show pixels on the screen at the end. But often, paragraph, text layo…
Regarding tables, this here https://www.npmjs.com/package/pdf-table-extractor does a very good job at table interpretation and works on top of pdf.js.
I also didn’t say what works better or worse, neither do I go into PDF being good or bad.
I simply said that a ton of problems have been covered by
Earlier quoted context omitted.
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…
> PDFs inherently are a markup / xml format This is false. PDFs are an object graph containing imperative-style drawing instructions (among many other things). There’s a way to add structural information on top (akin to an HTML document structure), but that’s completely optional and only serves as auxiliary metadata, it’s not at the core of the PDF format.
Indeed. Therein lies the rub.
Why?
Because no matter the fact that I've spent several years of my latent career crawling and parsing and outputting PDF data, I see now that pointing my LLLM stack at a directory of *.pdf just makes the invisible encoding of the object graph visible. It's a skeptical science.
The key transclusion may be to move from imperative to declarative tools or conditional to probabilistic tools, as many areas have in the last couple decades.
I've been following John Sterling's ocaml work for a while on related topics and the ideas floating around have been a good influence on me in forests and their forester which I found resonant given my own experience:
https://www.jonmsterling.com/index/index.xml
https://github.com/jonsterling/forest
I was gonna email john and ask whether it's still being worked on as I hope so, but I brought it up this morning as a way out of the noise that imperative programming PDF has been for a decade or more where turtles all the way down to the low-level root cause libraries mean that the high level imperative languages often display the exact same bugs despite significant differences as to what's being intended in the small on top of the stack vs the large on the bottom of the stack. It would help if "fitness for a particular purpose" decisions were thoughtful as to publishing and distribution but as the CFO likes to say, "Dave, that ship has already sailed." Sigh.
¯\_(ツ)_/¯
The better solution is to embed, in the PDF, the editable source document. This is easily done by LibreOffice. Embedding it takes very little space in general (because it compresses well), and then you have MUCH better information on what the text is and its meaning. It works just fine with existing PDF readers.
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…
The better solution is to embed, in the PDF, the editable source document. This is easily done by LibreOffice. Embedding it takes very little space in general (because it compresses well), and then you have MUCH better information on what the text is and its meaning. It works just fine with existing PDF readers.
What's the timeline for this solution to pay off
Since these are statistical classification problems, it seems like it would be worth trying some old-school machine learning (not an LLM, just an NN) to see how it compares with these manual heuristics.
I imagine that would work pretty well given an adequate and representative body of annotated sample data. Though that is also not easy to come by.
The problem of getting a representative body is (surprisingly) much harder than the annotation. I know. I spent quite some time years ago doing this.