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Ingesting PDFs and why Gemini 2.0 changes everything

• ChrisArchitect 2y ago
  Related: Gemini 2.0 is now available to everyone https://news.ycombinator.com/item?id=42950454 https://news.ycombinator.com/item?id=42950454
• cedws 2y ago
  90% accuracy +/- 10%? What could that be useful for, that’s awfully low.
  • lvzw 2y ago
    > accuracy is measured with the Needleman-Wunsch algorithm > Crucially, we’ve seen very few instances where specific numerical values are actually misread. This suggests that most of Gemini’s “errors” are superficial formatting choices rather than substantive inaccuracies. We attach examples of these failure cases below [1]. > Beyond table parsing, Gemini consistently delivers near-perfect accuracy across all other facets of PDF-to-markdown conversion. That seems fairly useful to me, no? Maybe not for mission critical applications, but for a lot of use cases, this seems to be good enough. I'm excited to try these prompts on my own later.
  • schainks 2y ago
    This is "good enough" for Banks to use when doing due diligence. You'd be surprised how much noise is in the system with the current state of the art: algorithms/web scrapers and entire buildings of humans in places like India.
    • ai-christianson 2y ago
      It's certainly pretty useful for discovery/information filtering purposes. I.e. searching for signal in the noise if you have a large dataset.
    • jjtheblunt 2y ago
      due diligence of this sort? https://en.wikipedia.org/wiki/Know_your_customer https://en.wikipedia.org/wiki/Know_your_customer
      • schainks 2y ago
        No, I mean services like Bloomberg. KYC is an API you can pay for now. Works pretty well for the price, IIRC over 10k/month or something.
  • MattDaEskimo 2y ago
    Switching from manual data entry to approval
  • summerlight 2y ago
    I guess 90% is for "benchmark", which is typically tailored to be challenging to parse.
  • serjester 2y ago
    Author here — measuring accuracy in table parsing is surprisingly challenging. Subtle, almost imperceptible differences in how a table is parsed may not affect the reader's understanding but can significantly impact benchmark performance. For all practical purposes, I'd say it's near perfect (also keep in mind the benchmark is on very challenging tables).
  • raunakchowdhuri 2y ago
    would encourage you to take a look at some of the real data here! https://huggingface.co/spaces/reducto/rd_table_bench https://huggingface.co/spaces/reducto/rd_table_bench you'll find that most of the errors here are structural issues with the table or inability to parse some special characters. tables can get crazy!
  • mattnewton 2y ago
    having seen some of these tables, I would guess that's probably above a layperson's score . Some are very complicated or just misleadingly structured.
• Havoc 2y ago
  Been toying with the flash model. Not the top model, but think it'll see plenty use due to the details. Wins on things other than top of benchmark logs * Generous free tier * Huge context window * Lite version feels basically instant However * Lite model seems more prone to repeating itself / looping * Very confusing naming e.g. {model}-latest worked for 1.5 but now its {model}-001? The lite has a date appended, the non-lite does not. Then there is exp and thinking exp...which has a date. wut?
  • ai-christianson 2y ago
    > * Huge context window But how well does it actually handle that context window? E.g. a lot of models support 200K context, but the LLM can only really work with ~80K or so of it before it starts to get confused.
    • summerlight 2y ago
      My experience is that Gemini works relatively well on larger contexts. Not perfect, but more reliable.
    • Havoc 2y ago
      I'm sure someone will do a haystack test, but from my casual testing it seems pretty good
    • asadm 2y ago
      it works REALLY well. I have used it to dump many references codes and then help me write a new modules etc. I have gone up to 200k tokens I think with no problems in recall.
      • ai-christianson 2y ago
        Awesome. Models that can usefully leverage such large context windows are rare at this point. Something like this opens up a lot of use cases.
    • f38zf5vdt 2y ago
• daemonologist 2y ago
  I wonder how this compares to open source models (which might be less accurate but even cheaper if self-hosted?), e.g. Llama 3.2. I'll see if I can run the benchmark. Also regarding the failure case in the footnote, I think Gemini actually got that right (or at least outperformed Reducto) - the original document seems to have what I call a "3D" table where the third axis is rows within each cell, and having multiple headers is probably the best approximation in Markdown.
  • mediaman 2y ago
    Everything I tried previously had very disappointing results. I was trying to get rid of Azure's DocumentIntelligence, which is kind of expensive at scale. The models could often output a portion of a table, but it was nearly impossible to get them to produce a structured output of a large table on a single page; they'd often insert "...rest of table follows" and similar terminations, regardless of different kinds of prompting. Maybe incremental processing of chunks of the table would have worked, with subsequent stitching, but if Gemini can just process it that would be pretty good.
• fecal_henge 2y ago
  Is there an AI platform where I can paste a snip of a graph and it will generate a n th order polynomial regression for me of the trace?
  • CamperBob2 2y ago
    Either ChatGPT o4 or one of the newer Google models should handle that, since it's a pretty common task. Actually there have been online curve fitters for several years that work pretty well without AI, such as https://curve.fit/ https://curve.fit/ and https://www.standardsapplied.com/nonlinear-curve-fitting-calculator.html https://www.standardsapplied.com/nonlinear-curve-fitting-cal... . I'd probably try those first, since otherwise you're depending on the language model to do the right thing automagically.
  • potatoman22 2y ago
    I've had decent luck using some of the reasoning models for this. It helps if you task them with identifying where the points on the graph are first.
• bt3 2y ago
  One major takeaway that matches my own investigation is that Gemini 2.0 still materially struggles with bounding boxes on digital content. Google has published[1] some great material on spatial understanding and bounding boxes on photography, but identifying sections of text or digital graphics like icons in a presentation is still very hit and miss. -- [1]: https://github.com/google-gemini/cookbook/blob/a916686f95f43aaef200875ac7174082dbdc4e76/quickstarts/Spatial_understanding.ipynb https://github.com/google-gemini/cookbook/blob/a916686f95f43...
  • maeil 2y ago
    Have you seen any models that perform better at this? I last looked into this a year ago but at the time they were indeed quite bad at it across the board.
• scottydelta 2y ago
  This is what I am trying to figure out how to solve. My problem statement is: - Injest PDFs, summarize, and extract important information. - Have some way to overlay the extracted information on the pdf in the UI. - User can provide feedback on the overlaid info by accepting or rejecting the highlights as useful or not. - This info goes back in to the model for reinforced learning. Hoping to find something that can make this more manageable.
  • baxtr 2y ago
    Have you tried cursor or replit for this?
  • cccybernetic 2y ago
    Most PDF parsers give you coordinate data (bounding boxes) for extracted text. Use these to draw highlights over your PDF viewer - users can then click the highlights to verify if the extraction was correct. The tricky part is maintaining a mapping between your LLM extractions and these coordinates. One way to do it would be with two LLM passes: 1. First pass: Extract all important information from the PDF 2. Second pass: "Hey LLM, find where each extraction appears in these bounded text chunks" Not the cheapest approach since you're hitting the API twice, but it's straightforward!
    • Jimmc414 2y ago
      Here's a PR thats not accepted yet for some reason that seems to be having some success with the bounding boxes https://github.com/getomni-ai/zerox/pull/44 https://github.com/getomni-ai/zerox/pull/44 Related to https://github.com/getomni-ai/zerox/issues/7 https://github.com/getomni-ai/zerox/issues/7
• exabrial 2y ago
  You know what'd be fucking nice? The ability to turn Gemini off.
  • pmarreck 2y ago
    hAIters gonna hAIte
• jfkrrorj 2y ago
  [flagged]
• fngjdflmdflg 2y ago
  >Unfortunately Gemini really seems to struggle on this, and no matter how we tried prompting it, it would generate wildly inaccurate bounding boxes This is what I have found as well. From what I've read, LLMS do not work well with images for specific details due to image encoders which are too lossy. (No idea if this is actually correct.) For now I guess you can use regular OCR to get bounding boxes.
  • minimaxir 2y ago
    Modern multimodal encoders for LLMs are fine/not lossy since they do not resize to a small size and can handle arbitrary sizes, although some sizes are obviously better represented in the training set. A 8.5" x 11" paper would be common. I suspect the issue is prompt engineering related. > Please provide me strict bounding boxes that encompasses the following text in the attached image? I'm trying to draw a rectangle around the text. > - Use the top-left coordinate system > - Values should be percentages of the image width and height (0 to 1) LLMs have enough trouble with integers (since token-wise integers and text representation of integers are the same), high-precision decimals will be even worse. It might be better to reframe the problem as "this input document is 850 px x 1100 px, return the bounding boxes as integers" then parse and calculate the decimals later.
    • fngjdflmdflg 2y ago
      Just tried this and it did not appear to work for me. Prompt: >Please provide me strict bounding boxes that encompasses the following text in the attached image? I'm trying to draw a rectangle around the text. > - Use the top-left coordinate system >this input document is 1080 x 1236 px. return the bounding boxes as integers
      • minimaxir 2y ago
        "Might" being the operative word, particularly with models that have less prompt adherence. There's a few other prompt massaging tricks beyond the scope of a HN comment, the decimal issue is just one optimization.
      • 2y ago
• coderstartup 2y ago
  Following this post
• cubefox 2y ago
  Why is Gemini Flash so much cheaper than other models here?
  • mattnewton 2y ago
    probably a mix of economies of scale (google workspace and search are already massive customers of these models meaning the build out is already there), and some efficiency dividends from hardware r&d (google has developed the model and the TPU hardware purpose built to run it almost in parallel)
• lazypenguin 2y ago
  I work in fintech and we replaced an OCR vendor with Gemini at work for ingesting some PDFs. After trial and error with different models Gemini won because it was so darn easy to use and it worked with minimal effort. I think one shouldn't underestimate that multi-modal, large context window model in terms of ease-of-use. Ironically this vendor is the best known and most successful vendor for OCR'ing this specific type of PDF but many of our requests failed over to their human-in-the-loop process. Despite it not being their specialization switching to Gemini was a no-brainer after our testing. Processing time went from something like 12 minutes on average to 6s on average, accuracy was like 96% of that of the vendor and price was significantly cheaper. For the 4% inaccuracies a lot of them are things like the text "LLC" handwritten would get OCR'd as "IIC" which I would say is somewhat "fair". We probably could improve our prompt to clean up this data even further. Our prompt is currently very simple: "OCR this PDF into this format as specified by this json schema" and didn't require some fancy "prompt engineering" to contort out a result. Gemini developer experience was stupidly easy. Easy to add a file "part" to a prompt. Easy to focus on the main problem with weirdly high context window. Multi-modal so it handles a lot of issues for you (PDF image vs. PDF with data), etc. I can recommend it for the use case presented in this blog (ignoring the bounding boxes part)!
  • cess11 2y ago
    What hardware are you using to run it?
    • kccqzy 2y ago
      The Gemini model isn't open so it does not matter what hardware you have. You might have confused Gemini with Gemma.
      • cess11 2y ago
        OK, I see, pity. I'm interested in similar applications but in contexts where the material is proprietary and might contain PII.
  • panarky 2y ago
• deleted 2y ago
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• ein0p 2y ago
  > Why Gemini 2.0 Changes Everything Clickbait. It doesn't change "everything". It makes ingestion for RAG much less expensive (and therefore feasible in a lot more scenarios), at the expense of ~7% reduction in accuracy. Accuracy is already rather poor even before this, however, with the top alternative clocking in at 0.9. Gemini 2.0 is 0.84, although the author seems to suggest that the failure modes are mostly around formatting rather than e.g. mis-recognition or hallucinations. TL;DR: is this exciting? If you do RAG, yes. Does it "change everything" nope. There's still a very long way to go. Protip for model designers: accuracy is always in greater demand than performance. A slow model that solves the problem is invariably better than a fast one that fucks everything up.
  • rvz 2y ago
    In this use-case, accuracy is non-negotiable with zero room for any hallucination. Overall it changes nothing.
    • ein0p 2y ago
      And people always have a hard time understanding what a certain degree of accuracy actually means. E.g. when you hear that a speech recognition system has 95% accuracy (5% WER), it means that it gets every 19th word wrong. That's abysmally bad by human standards - errors in every other sentence. That does not mean it's useless, but you do need to understand very clearly what you're dealing with, and what those errors might do to the rest of your system.
• cccybernetic 2y ago
  Shameless plug: I'm working on a startup in this space. But the bounding box problem hits close to home. We've found Unstructured's API gives pretty accurate box coordinates, and with some tweaks you can make them even better. The tricky part is implementing those tweaks without burning a hole in your wallet.
  • xiaofei_ 2y ago
    How is their API priced? I checked a few months ago and remembered it being expensive.
• sho_hn 2y ago
  Remember all the hyperbole a year ago on how Google was failing and over?
  • latexr 2y ago
    Anyone who cries “<service> is dead” after some new technology is introduced is someone you can safely ignore. For ever. They’re hyperbolic clout chasers who will only ever be right by mistake. As if, when ChatGPT was introduced, Google would just stay still, cross their arms, and say “well, this is based on our research paper but there’s nothing we can do, going to just roll over and wait for billions of dollars to run out, we’re truly doomed”. So unbelievably stupid.
• pockmarked19 2y ago
  Now, I could look at this relatively popular post about Google and revise my opinion of HN as an echo chamber, but I’m afraid it’s just that the downvote loving HNers weren’t able to make the cognitive leap from Gemini to Google.
• resource_waste 2y ago
  Google's models have historically been total disappointments compared to chatGPT4. Worse quality, wont answer medical questions either. I suppose I'll try it again, for the 4th or 5th time. This time I'm not excited. I'm expecting it to be a letdown.
• beklein 2y ago
  Great article, I couldn't find any details about the prompt... only the snippets of the `CHUNKING_PROMPT` and the `GET_NODE_BOUNDING_BOXES_PROMPT`. Is there is any code example with a full prompt available from OP, or are there any references (such as similar GitHub repos) for those looking to get started within this topic? Your insights would be highly appreciated.
• nickandbro 2y ago
  I think very soon a new model will destroy whatever startups and services are built around document ingestion. As in a model that can take in a pdf page as a image and transcribe it to text with near perfect accuracy.
  • depr 2y ago
    I think the Azure Document Intelligence, Google Document AI and Amazon Textract are among the best if not the best services though and they offer these models.
    • nnurmanov 2y ago
      I have not tested Azure Document Intelligence, Google Document AI, but AWS Textract, LLamaparse, Unstructured and Omni made to my shortlist. I have not tested Docling, as I could not install it on my Windows laptop.
  • layer8 2y ago
    Extracting plain text isn’t that much of a problem, relatively speaking. It’s interpreting more complex elements like nested lists, tables, side bars, footnotes/endnotes, cross-references, images and diagrams where things get challenging.
    • visarga 2y ago
      OCR is not 100% either. Reading order is also fragile, it might OCR the word but mess up the line structure.
• matthest 2y ago
  This is completely tangential, but does anyone know if AI is creating any new jobs? Thinking of the OCR vendors who get replaced. Where might they go? One thing I can think of is that AI could help the space industry take off. But wondering if there are any concrete examples of new jobs being created.
  • visarga 2y ago
    > Thinking of the OCR vendors who get replaced. Where might they go? We are solving more complicated document types, in more languages, longer in size. The scope of work expanded a lot.
• rjurney 2y ago
  I've been using NotebookLM powered by Gemini 2.0 for three projects and it is _really powerful_ for comprehending large corpuses you can't possibly read and thinking informed by all your sources. It has solid Q&A. When you ask a question or get a summary you like [which often happens] you can save it as a new note, putting it into the corpus for analysis. In this way your conclusions snowball. Yes, this experience actually happens and it is beautiful. I've tried Adobe Acrobat AI for this and it doesn't work yet. NotebookLM is it. The grounding is the reason it works - you can easily click on anything and it will take you to the source to verify it. My only gripe is that the visual display of the source material is _dogshit ugly_, like exceptionally so. Big blog pink background letters in lines of 24 characters! :) It has trouble displaying PDF columns, but at least it parses them. The ugly will change I'm sure :) My projects are setup to let me bridge the gaps between the various sources and synthesize something more. It helps to have a goal and organize your sources around that. If you aren't focused, it gets confused. You lay the groundwork in sources and it helps you reason. It works so well I feel _tender_ towards it :) Survey papers provide background then you add specific sources in your area of focus. You can write a profile for how you would like NotebookLM to think - which REALLY helps out. They are: * The Stratigrapher - A Lovecraftian short story about the world's first city. All of Seton Lloyd/Faud Safar's work on Eridu. Various sources on Sumerian culture and religion All of Lovecraft's work and letters. Various sources about opium Some articles about nonlinear geometries * FPGA Accelerated Graph Analytics An introduction to Verilog Papers on FPGAs and graph analytics Papers on Apache Spark architecture Papers on GraphFrames and a related rant I created about it and graph DBs A source on Spark-RAPIDS Papers on subgraph matching, graphlets, network motifs Papers on random graph models * Graph machine learning notebook without a specific goal, which has been less successful. It helps to have a goal for the project. It got confused by how broad my sources were. I would LOVE to share my projects with you all, but you can only share within a Google Workspaces domain. It will be AWESOME when they open this thing up :)
• deleted 2y ago
  [deleted]
• ratedgene 2y ago
  Is this something we can run locally? if so what's the license?
  • xnx 2y ago
    Gemini are Google cloud/service models. Gemma are the Google local models.
    • ratedgene 2y ago
      Ok got it, thanks. Is it a direct mapping?
• __jl__ 2y ago
  The numbers in the blog post seem VERY inaccurate. Quick calculation: Input pricing: Image input in 2.0 Flash is $0.0001935. Let's ignore the prompt. Output pricing: Let's assume 500 token per page, which is $0.0003 Cost per page: $0.0004935 That means 2,026 pages per dollar. Not 6,000! Might still be cheaper than many solutions but I don't see where these numbers are coming from. By the way, image input is much more expensive in Gemini 2.0 even for 2.0 Flash Lite. Edit: The post says batch pricing, which would be 4k pages based on my calculation. Using batch pricing is pretty different though. Great if feasible but not practical in many contexts.
  • serjester 2y ago
    Correct, it's with batching Vertex pricing with slightly lower output tokens per page since a lot of pages are somewhat empty in real world docs - I wanted a fair comparison to providers that charge per page. Regardless of what assumptions you use - it's still an order of magnitude + improvement over anything else.
• nothrowaways 2y ago
  Cool
• anirudhb99 2y ago
  thanks a ton for all the amazing feedback on this thread! if (a) you have document understanding use cases that you'd like to use gemini for (the more aspirational the better) and/or (b) there are loss cases for which gemini doesn't work well today, please feel free to email anirudhbaddepu@google.com and we'd love to help get your use case working & improve quality for our next series of model updates!
  • nabla9 2y ago
    What if you need scan pages from thick paper books or binded documents without specialized book scanner? I have two user cases in mind: 1. Photographs of open book. 2. Having video feed of open book where someone flips pages manually.
• raunakchowdhuri 2y ago
  CTO of Reducto here. Love this writeup! We’ve generally found that Gemini 2.0 is a great model and have tested this (and nearly every VLM) very extensively. A big part of our research focus is incorporating the best of what new VLMs offer without losing the benefits and reliability of traditional CV models. A simple example of this is we’ve found bounding box based attribution to be a non-negotiable for many of our current customers. Citing the specific region in a document where an answer came from becomes (in our opinion) even MORE important when using large vision models in the loop, as there is a continued risk of hallucination. Whether that matters in your product is ultimately use case dependent, but the more important challenge for us has been reliability in outputs. RD-TableBench currently uses a single table image on a page, but when testing with real world dense pages we find that VLMs deviate more. Sometimes that involves minor edits (summarizing a sentence but preserving meaning), but sometimes it’s a more serious case such as hallucinating large sets of content. The more extreme case is that internally we fine tuned a version of Gemini 1.5 along with base Gemini 2.0, specifically for checkbox extraction. We found that even with a broad distribution of checkbox data we couldn’t prevent frequent checkbox hallucination on both the flash (+17% error rate) and pro model (+8% error rate). Our customers in industries like healthcare expect us to get it right, out of the box, deterministically, and our team’s directive is to get as close as we can to that ideal state. We think that the ideal state involves a combination of the two. The flexibility that VLMs provide, for example with cases like handwriting, is what I think will make it possible to go from 80 or 90 percent accuracy to some number very close 99%. I should note that the Reducto performance for table extraction is with our pre-VLM table parsing pipeline, and we’ll have more to share in terms of updates there soon. For now, our focus is entirely on the performance frontier (though we do scale costs down with volume). In the longer term as inference becomes more efficient we want to move the needle on cost as well. Overall though, I’m very excited about the progress here. --- One small comment on your footnote, the evaluation script with Needlemen-Wunsch algorithm doesn’t actually consider the headers outputted by the models and looks only at the table structure itself.
  • noja 2y ago
    > deterministically How are you planning to do this?
• jbarrow 2y ago
  > Unfortunately Gemini really seems to struggle on this, and no matter how we tried prompting it, it would generate wildly inaccurate bounding boxes Qwen2.5 VL was trained on a special HTML format for doing OCR with bounding boxes. [1] The resulting boxes aren't quite as accurate as something like Textract/Surya, but I've found they're much more accurate than Gemini or any other LLM. [1] https://qwenlm.github.io/blog/qwen2.5-vl/ https://qwenlm.github.io/blog/qwen2.5-vl/
• xena 2y ago
  I really wish that Google made an endpoint that's compatible with the OpenAI API. That'd make trying Gemini in existing flows so much easier.
  • kurtoid 2y ago
    Is that not this? https://ai.google.dev/api/compatibility https://ai.google.dev/api/compatibility
  • myko 2y ago
    I believe this is already the case, at least the Python libraries are compatible, if not recommended for more than just trying things out: https://ai.google.dev/gemini-api/docs/openai https://ai.google.dev/gemini-api/docs/openai
    • msp26 2y ago
      How well do they work when you want to do things like grounding with search?
• devmor 2y ago
  I think this is one of the few functional applications of LLMs that is really undeniably useful. OCR has always been “untrustworthy” (as in you cannot expect it to be 100% correct and know you must account for that) and we have long used ML algorithms for the process.
  • nnurmanov 2y ago
    It is not OCR to blame, when you have garbage in you should not expect anything of high quality, especially with handwriting and tables and different languages. Even human beings fail to understand some documents (see doctor's prescriptions)
    • devmor 2y ago
      If OCR is a solution designed to recognize documents and it does not recognize all documents, then it is an imperfect solution. That is not to say there is a perfect solution, but it is still the fault of the solution.
      • nnurmanov 2y ago
        E.g. oftentimes there is l and I (capital I), this may be an issue for OCR. The perfect case is when there is a PDF document and data embedded as XML data, but unfortunately it is not the case.
• bambax 2y ago
  I'm building a system that does regular OCR and outputs layout-following ASCII; in my admittedly limited tests it works better than most existing offerings. It will be ready for beta testing this week or the next, and I will be looking for beta testers; if interested please contact me!
• siquick 2y ago
  Strange that LlamaParse is mentioned in the pricing table but not the results. We’ve used them to process a lot of pages and it’s been excellent each time.
• zoogeny 2y ago
  Orthogonal to this post, but this just highlights the need for a more machine readable PDF alternative. I get the inertia of the whole world being on PDF. And perhaps we can just eat the cost and let LLMs suffer the burden going forwards. But why not use that LLM coding brain power to create a better overall format? I mean, do we really see printing things out onto paper something we need to worry about for the next 100 years? It reminds me of the TTY interface at the heart of Linux. There was a time it all made sense, but can we just deprecate it all now?
  • layer8 2y ago
    PDF does support incorporating information about the logical document structure, aka Tagged PDF. It’s optional, but recommended for accessibility (e.g. PDF/UA). See chapters 14.7–14.8 in [1]. Processing PDF files as rendered images, as suggested elsewhere in this thread, can actually dramatically lose information present in the PDF. Alternatively, XML document formats and the like do exist. Indeed, HTML was supposed to be a document format. That’s not the problem. The problem is having people and systems actually author documents in that way in an unambiguous fashion, and having a uniform visual presentation for it that would be durable in the long term (decades at least). PDF as a format persists because it supports virtually every feature under the sun (if authors care to use them), while largely guaranteeing a precisely defined visual presentation, and being one of the most stable formats. [1] https://opensource.adobe.com/dc-acrobat-sdk-docs/pdfstandard https://opensource.adobe.com/dc-acrobat-sdk-docs/pdfstandard...
    • zoogeny 2y ago
      I'm not suggesting we re-invent RDF or any other kind of semantic web idea. And the fact that semantic data can be stored in a PDF isn't really the problem being solved by tools such as these. In many cases, PDF is used for things like scanned documents where adding that kind of metadata can't really be done manually - in fact the kinds of tools suggested in the post would be useful for adding that metadata to the PDF after scanning (for example). Imagine you went to a government office looking for some document from 1930s, like an ancestors marriage or death certificate. You might want to digitize a facsimile of that using a camera or a scanner. You have a lot of options to store that, JPG, PNG, PDF. You have even more options to store the metadata (XML, RDF, TXT, SQLite, etc.). You could even get fancy and zip up an HTML doc alongside a directory of images/resources that stitched them all together. But there isn't really a good standard format to do that. It is the second part of you post that stands out - the kitchen sink nature of PDFs that make them so terrible. If they were just wrappers for image data, formatted in a way that made printing them easy, I probably wouldn't dislike them.
• jibuai 2y ago
  I've been working on something similar the past couple months. A few thoughts: - A lot of natural chunk boundaries span multiple pages, so you need some 'sliding window' mechanism for the best accuracy. - Passing the entire document hurts throughput too much due to the quadratic complexity of attention. Outputs are also much worse when you use too much context. - Bounding boxes can be solved by first generating boxes using tradition OCR / layout recognition, then passing that data to the LLM. The LLM can then link it's outputs to the boxes. Unfortunately getting this reliable required a custom sampler so proprietary models like Gemini are out of the question.
• dasl 2y ago
  How does the Gemini OCR perform against non-English language text?
• an_aparallel 2y ago
  Has anyone in the AEC industry who's reading this worked out a good way to get Bluebeam MEP, electrical layouts into Revit (LOD 200-300). Have seen MarkupX as a paid option, but it seems some AI in the loop can greatly speed up exception handling, encode family placement to certain elevations based on building code docs....
• sensecall 2y ago
  This is super interesting. Would this be suitable for ingesting and parsing wildly variable unstructured data into a structured schema?
• kbyatnal 2y ago
  It's clear that OCR & document parsing are going to be swallowed up by these multimodal models. The best representation of a document at the end of the day is an image. I founded a doc processing company [1] and in our experience, a lot of the difficulty w/ deploying document processing into production is when accuracy requirements are high (> 97%). This is because OCR and parsing is only one part of the problem, and real world use cases need to bridge the gap between raw outputs and production-ready data. This requires things like: - state-of-the-art parsing powered by VLMs and OCR - multi-step extraction powered by semantic chunking, bounding boxes, and citations - processing modes for document parsing, classification, extraction, and splitting (e.g. long documents, or multi-document packages) - tooling that lets nontechnical members quickly iterate, review results, and improve accuracy - evaluation and benchmarking tools - fine-tuning pipelines that turn reviewed corrections —> custom models Very excited to get test and benchmark Gemini 2.0 in our product, very excited about the progress here. [1] https://extend.app/ https://extend.app/
  • anon373839 2y ago
    > It's clear that OCR & document parsing are going to be swallowed up by these multimodal models. I don’t think this is clear at all. A multimodal LLM can and will hallucinate data at arbitrary scale (phrases, sentences, etc.). Since OCR is the part of the system that extracts the “ground truth” out of your source documents, this is an unacceptable risk IMO.
    • ALittleLight 2y ago
      Seems like you could solve hallucinations by repeating the task multiple times. Non-hallucinations will be the same. Hallucinations will be different. Discard and retry hallucinated sections. This increases cost by a fixed multiple, but if cost of tokens continues to fall that's probably perfectly fine.
    • nnurmanov 2y ago
      If you see above, someone is using a second and even third LLM to correct LLM outputs, I think it is the way to minimize hallucinations.
• ThinkBeat 2y ago
  Hmm I have been doing a but if this manually lately for a personal project. I am working on some old books that are far past any copyright, but they are not available anywhere on the net. (Being in Norwegian m makes a book a lot more obscure) so I have been working on creating ebooks out of them. I have a scanner, and some OCR processes I run things through. I am close to 85% from my automatic process. The pain of going from 85% to 99% though is considerable. (and in my case manual) (well Perl helps) I went to try this AI on one of the short poem manufscript I have. I told the prompt I wanted PDF to Markdown, it says sure go ahead give me the pdf. I went upload it. It spent a long time spinning. then a quick messages comes up, something like "Failed to count tokens" but it just flashes and goes away. I guess the PDF is too big? Weird though, its not a lot of pages.
  • sumedh 2y ago
    Take a screenshot of the pdf page and give that to the LLM and see if it can be processed. Your PDF might have some quirks inside which the LLM cannot process.
  • stigz 2y ago
    I experienced something similar. My use case is I need to summarize bank statements (sums, averages, etc.). Gemini wouldn't do it, it said too many pages. When I asked the max number of supported pages, it says max is 14 pages. Attempted on both 2.0 flash and 2.0 pro in VertexAI console.
    • shijithpk 2y ago
      Try with https://aistudio.google.com https://aistudio.google.com Think the page limit is a vertex thing The only limit in reality is the number of input tokens taken to parse the pdf. If those tokens + tokens for the rest of your prompt are under the context window limit, you're good.
• rudolph9 2y ago
  We parse millions of PDFs using Apache Tika and process about 30,000 per dollar of compute cost. However, the structured output leaves something to be desired, and there are a significant number of pages that Tika is unable to parse. https://tika.apache.org/ https://tika.apache.org/
  • rudolph9 2y ago
    Under the hood tika uses tesseract for ocr parsing. For clarity this all works surprisingly well generally speaking and it’s pretty easy to run your self and order of magnitude cheaper than most services out there. https://tesseract-ocr.github.io/tessdoc/ https://tesseract-ocr.github.io/tessdoc/
• nottorp 2y ago
  Will 2.0.1 also change everything? How about 2.0.2? How about Llama 13.4.0.1? This is tiring. It's always the end of the world when they release a new version of some LLM.
  • nnurmanov 2y ago
    prompt and pray, this is my default mode while working with LLMs
• llm_trw 2y ago
  This is using exactly the wrong tools at every stage of the OCR pipeline, and the cost is astronomical as a result. You don't use multimodal models to extract a wall of text from an image. They hallucinate constantly the second you get past perfect 100% high-fidelity images. You use an object detection model trained on documents to find the bounding boxes of each document section as _images_; each bounding box comes with a confidence score for free. You then feed each box of text to a regular OCR model, also gives you a confidence score along with each prediction it makes. You feed each image box into a multimodal model to describe what the image is about. For tables, use a specialist model that does nothing but extract tables—models like GridFormer that aren't hyped to hell and back. You then stitch everything together in an XML file because Markdown is for human consumption. You now have everything extracted with flat XML markup for each category the object detection model knows about, along with multiple types of probability metadata for each bounding box, each letter, and each table cell. You can now start feeding this data programmatically into an LLM to do _text_ processing, where you use the XML to control what parts of the document you send to the LLM. You then get chunking with location data and confidence scores of every part of the document to put as meta data into the RAG store. I've build a system that read 500k pages _per day_ using the above completely locally on a machine that cost $20k.
  • ck_one 2y ago
    What object detection model do you use?
    • a3w 2y ago
      Is tesseract even ML based? Oh, this piece of software is more than 19 years old, perhaps there are other ways to do good, cheap OCR now. Does Gemini have an OCR library, internally? For other LLMs, I had the feeling that the LLM scripts a few lines of python to do the actual heavy lifting with a common OCR framework.
    • llm_trw 2y ago
      Custom trained yolo v8. I've moved on since then and the work was done in 2023. You'd get better results for much less today.
• oedemis 2y ago
  there is also https://ds4sd.github.io/docling/ https://ds4sd.github.io/docling/ from ibm research which is mit license and track bounding boxes as rich json format
  • pbronez 2y ago
    Docling has worked well for me. It handles scenarios that crashed ChatGPT Pro. Only problem is it's super annoying to install. When I have a minute I might package it for homebrew.
    • disqard 2y ago
      Did you compare it to tesseract? If it's superior (esp. for scans with text flowing around image boxes), and if you do end up packaging it up for brew, know that there's at least one developer who will benefit from your work (for a side-project, but that goes without saying). Thanks in advance!
• sergiotapia 2y ago
  The article mentions OCR, but you're sending a PDF how is that OCR? Or is this is mistake? What if you send photos of the pages, that would be true OCR - does the performance and price remain the same? If so this unlocks a massive workflow for us.
• xnx 2y ago
  Glad Gemini is getting some attention. Using it is like a superpower. There are so many discussions about ChatGTP, Claude, DeepSeek, Llama, etc. that don't even mention Gemini.
  • throwaway314155 2y ago
    Google had a pretty rough start compared to ChatGPT, Claude. I suspect that left a bad taste in many people's mouths. In particular because evaluating so many LLM's is a lot of effort on its own. Llama and DeepSeek are no-brainers; the weights are public.
    • beastman82 2y ago
      No brainer if you're sitting on a >$100k inference server.
      • throwaway314155 2y ago
        Sure, that's fair. If you're aiming for state of the art performance. Otherwise, you can get close and do it on reasonably priced hardware by using smaller distilled and/or quantized variants of llama/r1. Really though I just meant "it's a no-brainer that they are popular here on HN".
      • BoorishBears 2y ago
        I pay 78 cents an hour to host Llama.
        • beastman82 2y ago
          Vast? Specs?
          • BoorishBears 2y ago
            Runpod, 2xA40. Not sure why you think buying an entire inference server is a necessity to run these models.
• roywashere 2y ago
  I think it is very ironic that we chose to use PDF in many fields to archive data because it is a standard and because we would be able to open our pdf documents in 50 or 100 years time. So here we are just a couple of years later facing the challenge of getting the data out of our stupid PDF documents already!
  • esafak 2y ago
    It's not ironic. PDFs are a container, which can hold scanned documents as well as text. Scanned documents need OCR and to be analyzed for their layout. This is not a failing of the PDF format, but a problem inherent to working with print scans. I don't claim PDF is a good format. It is inscrutable to me.
    • scotty79 2y ago
      Pdf is a horrible format. Even if it contains plain text it has no concept of something as simple as paragraphs. One can wonder how much wonkiness of llms comes from errors in extracting language from pdfs. Adobe is the most harmful software development company in existence.
      • phoh 2y ago
        amen
• gapeslape 2y ago
  In my mind, Gemini 2.0 changes everything because of the incredibly long context (2M tokens on some models), while having strong reasoning capabilities. We are working on compliance solution (https://fx-lex.com https://fx-lex.com) and RAG just doesn’t cut it for our use case. Legislation cannot be chunked if you want the model to reason well about it. It’s magical to be able to just throw everything into the model. And the best thing is that we automatically benefit from future model improvements along all performance axes.
  • manmal 2y ago
    Maybe a dumb question, have you tried fine tuning on the corpus, and then adding a reasoning process (like all those R1 distillations)?
    • gapeslape 2y ago
      We haven't tried that, we might do that in the future. My intuition - not based on any research - is that recall should be a lot better from in context data vs. weights in the model. For our use case, precise recall is paramount.
  • pvo50555 2y ago
    What does "throw everything into the model" entail in your context? How much data are you able to feed into the model in a single prompt and on what hardware, if I may ask?
    • gapeslape 2y ago
      Gemini models run in the cloud, so there is no issue with hardware. The EU regulations typically include delegated acts, technical standards, implementation standards and guidelines. With Gemini 2.0 we are able to just throw all of this into the model and have it figure out. This approach gives way better results than anything we are able to achieve with RAG. My personal bet is that this is how the future will look like. RAG will remain relevant, but only for extremely large document corpuses.
• mateuszbuda 2y ago
  There’s AWS Bedrock Knowledge Base (Amazon proprietary RAG solution) which can digest PDFs and, as far as I tested it on real world documents, it works pretty well and is cost effective.
• erulabs 2y ago
  Hrm I've been using a combo of Textract (for bounding boxes) AI for understanding the contents of the document. Textract is excellent at bounding boxes and exact-text capture, but LLMs are excellent at understanding when a messy/ugly bit of a form is actually one question, or if there are duplicate questions etc. Correlating the two (Textract <-> AI) output is difficult, but another round of AI is usually good at that. Combined with some text-different scoring and logic, I can get pretty good full-document understanding of questions and answer locations. I've spent a pretty absurd amount of time on this and as of yet have not launched a product with it, but if anyone is interested I'd love to chat about the pipeline!
• pbronez 2y ago
  Wonder how this compares to Docling. So far that's been the only tool that really unlocked PDFs for me. It's solid but really annoying to install. https://ds4sd.github.io/docling/ https://ds4sd.github.io/docling/
• ritvikpandey21 2y ago
  ritvik here from pulse. everyone’s pretty much made the right points here, but wanted to emphasize that due to the llm architecture, they predict “the most probable text string” that corresponds to the embedding, not necessarily the exact text. this non-deterministicness is awful for customers deploying in production and a lot of our customers complained about this to us initially. the best approach is to build a sort-of “agent”-based VLM x traditional layout segmentation/reading order algos, which is what we’ve done and are continuing to do. we have a technical blog on this exact phenomena coming out in the next couple days, will attach it here when it’s out! check us out at https://www.runpulse.com https://www.runpulse.com
• twelve40 2y ago
  Isn't it amazing how one company invented a universally spread format that takes structured data from an editor (except images obviously) and converts it into a completely fucked-up unstructured form that then requires expensive voodoo magic to convert back into structured data.
  • sconeguy 2y ago
    What would it have taken to store the plain text in some meta field in the document. Argh, so annoying.
    • dkjaudyeqooe 2y ago
      PDF provide that capability, but editors don't produce it, probably because printing is though OS drivers that don't support it, or PDF generators that don't support it. Or they do support it but users don't know to check that option, or turn it off because it makes PDFs too large.
      • user_7832 2y ago
        Do you know what this field/type is called, and I’d any of the big names (MS/Adobe etc) support creating such PDFs?
        • bux93 2y ago
          OCR software like ABBY can spit out something called a "searchable PDF", which has a text layer underneath a picture of a scan. Otherwise, PDF has 'dictionaries' with arbitrary key-value pairs in them. The "Info" dictionary has some specific metadata fields like Author, and a "Font" dictionary embeds fonts, but you're free to use those dictionaries for whatever. There's also a standard to embed 'dublin core', rights management and custom metadata called XMP. Files can be embedded. You can also use comments, as PDF is a subset of postscript. When a PDF gets converted to PDF/A (by archiving software) or flattened/optimized, most of these are likely to be lost.
        • dkjaudyeqooe 2y ago
• throw7381 2y ago
  For data extraction from long documents (100k+ tokens) how does structured outputs via providing a json schema compare vs asking one question per field (in natural language)? Also I've been hearing good things regarding document retrieval about Gemini 1.5 Pro, 2.0 Flash and gemini-exp-1206 (the new 2.0 Pro?), which is the best Gemini model for data extraction from 100k tokens? How do they compare against Claude Sonnet 3.5 or the OpenAI models, has anyone done any real world tests?
• lifeisstillgood 2y ago
  Imagine there's no PostScript It's easy if you try No pdfs below us Above us only SQL Imagine all the people livin' for CSV
• freezed8 2y ago
  (disclaimer I am CEO of llamaindex, which includes LlamaParse) Nice article! We're actively benchmarking Gemini 2.0 right now and if the results are as good as implied by this article, heck we'll adapt and improve upon it. Our goal (and in fact the reason our parser works so well) is to always use and stay on top of the latest SOTA models and tech :) - we blend LLM/VLM tech with best-in-class heuristic techniques. Some quick notes: 1. I'm glad that LlamaParse is mentioned in the article, but it's not mentioned in the performance benchmarks. I'm pretty confident that our most accurate modes are at the top of the table benchmark - our stuff is pretty good. 2. There's a long tail of issues beyond just tables - this includes fonts, headers/footers, ability to recognize charts/images/form fields, and as other posters said, the ability to have fine-grained bounding boxes on the source elements. We've optimized our parser to tackle all of these modes, and we need proper benchmarks for that. 3. DIY'ing your own pipeline to run a VLM at scale to parse docs is surprisingly challenging. You need to orchestrate a robust system that can screenshot a bunch of pages at the right resolution (which can be quite slow), tune the prompts, and make sure you're obeying rate limits + can retry on failure.
  • rahimnathwani 2y ago
    Hi Jerry, How well does llamaparse work on foreign-language documents? I have pipeline for Arabic-language docs using Azure for OCR and GPT-4o-mini to extract structured information. Would it be worth trying llamaparse to replace part of the pipeline or the whole thing?
    • freezed8 2y ago
      yes! we have foreign language support for better OCR on scans. Here's some more details. Docs: https://docs.cloud.llamaindex.ai/llamaparse/features/parsing_options https://docs.cloud.llamaindex.ai/llamaparse/features/parsing... Notebook: https://github.com/run-llama/llama_parse/blob/main/examples/demo_languages.ipynb https://github.com/run-llama/llama_parse/blob/main/examples/...
      • rahimnathwani 2y ago
        What is disable_ocr=True for? Is it for documents that already have a text layer, that you don't want to OCR again?
• dwheeler 2y ago
  I wish more PDFs were generated as hybrid PDFs. These are PDFs that also include their original source material. Then you have a document whose format is fixed, but if you need more semantic information, there it is! LibreOffice makes this especially easy to do: https://wiki.documentfoundation.org/Faq/Writer/PDF_Hybrid https://wiki.documentfoundation.org/Faq/Writer/PDF_Hybrid
• KoolKat23 2y ago
  Okay I just checked/tried this out with my own use case at work and it's insane.
• airwaveai 2y ago
  Curious to see how well this works on technical/mechanical documentation (manuals parts list etc). Has any one tried? My company Airwave had to jump through all sorts of hoops to get accurate information for our use case: getting accurate info to the technicians in the field.
• geckel 2y ago
  How is it for image recognition/classification? OCR can be a huge chunk of the image classification pipeline. Presumably, it works just as well in this domain?
• jeswin 2y ago
  We've previously tried Sonnet in our PDF extraction pipelines. It was very, very accurate, gpt-4o did not come close. Its more expensive, however.
• jwr 2y ago
  I wish I could do this locally. I don't feel comfortable uploading all of my private documents to Google.
• throwaway31412 2y ago
  fds
• minimalengineer 2y ago
  Two years ago, I worked for a company that had its own proprietary AI system for processing PDFs. While the system handled document ingestion, its real value was in extracting and analyzing data to provide various insights. However, one key requirement was rendering documents in HTML with as close to a 1:1 likeness as possible. At the time, I evaluated multiple SDKs for both OCR and non-OCR PDF conversions, but none matched the accuracy of Adobe Acrobat’s built-in solution. In fact, at one point (don’t laugh), the company resorted to running Adobe Acrobat on a Windows machine with automation tools to handle the conversion. Using Adobe’s cloud service for conversion was not an option due to the proprietary nature of the PDFs. Additionally, its results were inconsistent and often worse compared to the desktop version of Adobe Acrobat! Given that experience, I see this primarily as an HTML/text conversion challenge. If Gemini 2.0 truly improves upon existing solutions, it would be interesting to see a direct comparison against popular proprietary tools in terms of accuracy.
• lyjackal 2y ago
  If the end goal is just rag or search over the pdfs, seems like ColPali based embedding search would be a good alternative here. Don’t process the PDFs, instead just search their image embedding directly. From what I understand, you also get a sort of attention as to what part of the image is being activated by the search.
• galvin 2y ago
  Somewhat tangential, but the EU has a directive mandating electronic invoicing for public procurement. One of the standards that has come out of that is EN 16931, also known as ZUGFeRD and Factur-X, which basically involves embedding an XML file with the invoice details inside a PDF/A. It allows the PDF to be used like a regular PDF but it also allows the government procurement platforms to reliably parse the contents without any kind of intelligence. It seems like a nice solution that would solve a lot of issues with ingesting PDFs for accounting if everyone somehow managed to agree a standard. Maybe if EN 16931 becomes more broadly available it might start getting used in the private sector too.
• GGByron 2y ago
  I've not followed the literature very closely for some time - what problem are they trying to solve in the first place? They write "for documents to be effectively used in RAG pipelines, they must be split into smaller, semantically meaningful chunks". Segmenting each page by paragraphs doesn't seem like a particularly hard vision problem, nor do I see why an OCR system would need to incorporate an LLM (which seem more like a demonstration of overfitting than a "language model" in any literal sense, going by ChatGPT). Perhaps I'm just out of the loop. Finally, I must point out that statements in the vein of "Why [product] 2.0 Changes Everything" are more often than not a load of humbug.
• hnuser435 2y ago
  Damn, I thought this was about the Gemini protocol. https://geminiprotocol.net/ https://geminiprotocol.net/
• rjcrystal 2y ago
  I work in healthcare domain, We've had great success converting printed lab reports (95%) to Json format using 1.5-Flash model. This post is really exciting for me. will definitely try out 2.0 models. The struggle which almost every ocr usecase faces is with handwritten documents(doctor prescriptions with bad handwriting) With gemini 1.5 flash we've had ~75-80% percent accuracy (based on random sampling by pharmacists). we're planning to improve this further by fine-tuning gemini models with medical data. What could be other alternative services/models for accurate handwriting ocr?
  • myaccountonhn 2y ago
    > We've had great success converting printed lab reports (95%) to Json format using 1.5-Flash model Sounds terrifying. How can you be sure that there were no conversion mistakes?
  • Havoc 2y ago
    How on earth is anyone ok with 75% accuracy in prescriptions context?!? Or medical anything That’s literally insane
    • martimchaves 2y ago
      I'm guessing that human accuracy may be lower or around that value, given that handwritten notes are generally difficult to read. A better metric for document parsing might be accuracy relative to human performance (how much better the LLM performs compared to a human).
    • knowaveragejoe 2y ago
      Nobody said they're okay with it, nor did they describe what they use the data for.
• eviks 2y ago
  What would change "everything" is if we managed to switch to "real" digital parseable formats instead of this dead tree emulation that buries all data before the arrival of AI...
• rp36 2y ago
  I'm failing to understanding the Ingesting part of the Gemini 2.0? Does Gemini provide the a process to convert PDFs to Markdown API OR the LLM APIs handle it with prompt "Extract the Attached PDF" using this API: https://ai.google.dev/gemini-api/docs/document-processing?lang=node https://ai.google.dev/gemini-api/docs/document-processing?la...
• jgleoj23 2y ago
  Gemini is amazing but I get this copyright error for some documents and I have a rate limit of just 10 requests per minute. Same issues with claude except the copyright error is called content warning.
• silverliver 2y ago
  We are driving full speed into a xerox 2.0 moment and this time we are doing so knowingly. At least with xerox, the errors were out of place and easy to detect by a human. I wonder how many innocent people will lose their lives or be falsely incarcerated because of this. I wonder if we will adapt our systems and procedures to account for hallucinations and "85%" accuracy. And no, outlawing use the use of AI or increasing liability with its use will have next to nothing to deter its misuse and everyone knows it. My heart goes out to the remaining 15%.
  • csomar 2y ago
    Humans accept a degree of error for convenience. (driving is one of them). But no, 15% is not the acceptable rate. More like 0.15% to 0.015% depending on the country.
  • anon373839 2y ago
    I love generative AI as a technology. But the worst thing about its arrival has been the reckless abandonment of all engineering discipline and common sense. It’s embarrassing.
  • sschueller 2y ago
    CCC talk about Xerox copiers changing numbers when doing OCR: https://media.ccc.de/v/31c3_-_6558_-_de_-_saal_g_-_201412282300_-_traue_keinem_scan_den_du_nicht_selbst_gefalscht_hast_-_david_kriesel https://media.ccc.de/v/31c3_-_6558_-_de_-_saal_g_-_201412282...
    • tomrod 2y ago
      Would be nice to get a translation for a broader audience, glad folks are reporting this out!
      • sschueller 2y ago
        There is a translated one: https://www.youtube.com/watch?v=zXXmhxbQ-hk https://www.youtube.com/watch?v=zXXmhxbQ-hk
• applgo443 2y ago
  Why are traditional OCRs better in terms of hallucination and confidence scores? Can we use logprobs of LLM as confidence scores?
  • dfeng 2y ago
    Traditional OCRs are trained for a single task: recognize characters. They do this through visual features (and sometimes there's an implicit (or even explicit) "language" model: see https://arxiv.org/abs/1805.09441 https://arxiv.org/abs/1805.09441). As such, the extent of their "hallucination", or errors, is when there's ambiguity in characters, e.g. 0 vs O (that's where the implicit language model comes in). Because they're trained with a singular purpose, you would expect their confidence scores (i.e. logprobs) to be well calibrated. Also, depending on the OCR model, you usually do a text detection (get bounding boxes) followed by a text recognition (read the characters), and so it's fairly local (you're only dealing with a small crop). On the other hand, these VLMs are very generic models – yes, they're trained on OCR tasks, but also a dozen of other tasks. As such, they're really good OCR models, but they tend to be not as well calibrated. We use VLMs at work (Qwen2-VL to be specific), and we don't find it hallucinates that often, but we're not dealing with long documents. I would assume that as you're dealing with a larger set of documents, you have a much larger context, which increases the chances of the model getting confused and hallucinating.
• otabdeveloper4 2y ago
  Well, probably not literally "everything".
• grandimam 2y ago
  Would you recommend using these large models for parsing sensitive data - probably say bank statements etc?
• diptanu 2y ago
  We started with using LLMs for parsing at Tensorlake (https://docs.tensorlake.ai https://docs.tensorlake.ai), tried Qwen, Gemini, OpenAI, pretty much everything under the sun. My thought was we could skip 5-6 years of development IDP companies have done on specialized models by going to LLMs. On information dense pages, LLMs often hallucinate half of the times, they have trouble understanding empty cells in tables, doesn't understand checkboxes, etc. We had to invest heavily into building a state of the art layout understanding model and finally a table structure understanding for reliability. LLMs will get there, but there are some ways to go there. Where they do well is in VQA type use cases, ask a question, very narrowly scoped, they will work much better than OCR+Layout models, because they are much more generalizable and flexible to use.
• mchadda_chunkr 2y ago
  Hi all - CEO of chunkr.ai here. The write-up and ensuing conversation are really exciting. I think out of everything mentioned here - the clear stand-out point is that document layout analysis (DLA) is the crux of the issue for building practical doc ingestion for RAG. (Note: DLA is the process of identifying and bounding specific segments of a document - like section headers, tables, formulas, footnotes, captions, etc.) Strap in - this is going to be a longy. We see a lot of people and products basically sending complete pages to LVLMs for converting to a machine-readable format, and for chunking. We tried this + it’s a possible configuration on chunkr as well. It has never worked for our customers, or during extensive internal testing across documents from a variety of verticals. Here are SOME of the common problems: - Most documents are dense. The model will not OCR everything and miss crucial parts. - A bunch of hallucinated content thats tough to catch. - Occasionally it will just refuse to give you anything. We’ve tried a bunch of different prompting techniques and the models return “<image>” or “||..|..” for an ENTIRE PAGE of content. Despite this - it’s obvious that these ginormous neural nets are great for complex conversions like tables and formulas to HTML/Markdown & LateX. They also work great for describing images and converting charts to tables. But that’s the thing - they can only do this if you can pull out these document features individually as cropped images and have the model focus on small snippets of the document rather than the full page. If you want knobs for speed, quality, and cost, the best approach is to work at a segment level rather than a page level. This is where DLA really shines - the downstream processing options are vast and can be fit to specific needs. You can choose what to process with simple + fast OCR (text-only segments like headers, paragraphs, captions), and what to send to a large model like Gemini (complex segments like tables, formulas, and images) - all while getting juicy bounding boxes for mapping citations. Combine this with solid reading order algos - and you get amazing layout-aware chunking that takes ~10ms. We made RAG apps ourselves and attempted to index all ~600 million pages of open-access research papers for https://lumina.sh https://lumina.sh. This is why we built Chunkr - and it needed to be Open Source. You can self-host our solution and process 4 pages per second, scaling up to 11 million pages per month on a single RTX 4090, renting this hardware on Runpod costs just $249/month ($0.34/hour). A VLM to do DLA sounds awesome. We've played around with this idea but found that VLMs don't come close to models where the architecture is solely geared toward these specific object detection tasks. While it would simplify the pipeline, VLMs are significantly slower and more resource-hungry - they can't match the speed we achieve on consumer hardware with dedicated models. Nevertheless, the numerous advances in the field are very exciting - big if true! A note on costs: There are some discrepancies between the API pricing of providers listed in this thread. Assuming 100000 pages + feature parity: Chunkr API - 200 pages for $1, not 100 pages AWS Textract - 40 pages for $1, not 1000 pages (No VLMs) Llama Parse - 13 pages for $1, not 300 A note on RD-Bench: We’ve been using Gemini 1.5 Pro for tables and other complex segments for a while, so the RD-bench is very outdated. We ran it again on a few hundred samples and got a 0.81 (also includes some notes on the bench itself). To the OP: it would be awesome if you could update your blog post! https://github.com/lumina-ai-inc/chunkr-table-rdbench/tree/main https://github.com/lumina-ai-inc/chunkr-table-rdbench/tree/m...
• iudqnolq 2y ago
  In what contexts is 0.84 ± 0.16 actually "nearly perfect"?
  • kym6464 2y ago
    I think they meant relative to the best other approach, which is Reducto’s given that they are the creators of the benchmark: Reducto's own model currently outperforms Gemini Flash 2.0 on this benchmark (0.90 vs 0.84). However, as we review the lower-performing examples, most discrepancies turn out to be minor structural variations that would not materially affect an LLM’s understanding of the table.
• kym6464 2y ago
  RE: the loss of bounding box information You can recover word-level bounding boxes and confidence scores by using a traditional OCR engine such as AWS Textract and matching the results to Gemini’s output – see https://docless.app https://docless.app for a demo (disclaimer: I am the founder)
• lyzml_AF 2y ago
  [dead]
• andrewshadura 2y ago
  > Crucially, we’ve seen very few instances where specific numerical values are actually misread. "Very few" is way too many. This means it cannot be trusted, especially when it comes to financial data.
• nsmurali 2y ago
  I have seen no decent program that can read, OCR, and analyze, and tabulate data correctly from very large PDF files with a lot of scanned information from different sources. I run my practice with pdf files- one for each patient. It is a treasure trove of actionable data. PDF filing in this manner allows me to finish my daily tasks in 4 hrs instead of 12 hrs! For sick patients who need information at the point of care, PDF has numerous advantages over usual hospital EHR portals, etc. If any smart Engineer/s are interested in working with me, please connect with me
  • 7thpower 2y ago
    I can help as can many others. Probably a good place to start though is with some of the more recent off the shelf solutions like trellis (I have no affiliation with them).
• memhole 2y ago
  I’ve been very reluctant to use closed source LLMs. This might actually convince me to use one. I’ve done so many attempts at pdf parsing over the years. It’s awful to deal with. 2 column format omg. Most don’t realize that pdfs contain instructions for displaying the document and the content is buried in there. It’s just always been a problematic format. So if it works, I’d be a fool not to use it.
• eichi 2y ago
  If is is a vendor work, you should probably hire person who are competitive in software engineering space. And do we actually need significant amount of processing as a solution? If this is the case, common markdowned public pdfs should be open-sourced. We shouldn't repeat other's work. Despite that, cheaper is better.
• lacoolj 2y ago
  Anyone know if there are uses of this with PHI? Most doctors still fax reports to each other and this would help a lot to drop the load on staff when receiving and categorizing/assigning to patients
• anonu 2y ago
  Ingesting PDFs accurately is a noble goal which will no doubt be solved as LLMs get better. However, I need to point out that the financial statement example used in the article already has a solution: iXBRL. Many financial regulators require you to publish heavily marked up statements with iXBRL. These markups reveal nuances in the numbers that OCRing a post processed table will not understand. Of course, financial documents are a narrow subset of the problem. Maybe the problem is with PDF as a format: Unfortunately PDFs lose that meta information when they are built from source documents. I can't help but feel that PDFs could probably be more portable as their acronym indicates.
  • tomrod 2y ago
    Just call out -- even better, this library (even in active development) is blowing every other SEC tool I've found out the of the water https://github.com/dgunning/edgartools https://github.com/dgunning/edgartools
• mehulashah 2y ago
  (Disclosure, CEO of Aryn (https://aryn.ai/ https://aryn.ai/) here) Good post. VLM models are improving and Gemini 2.0 definitely changes the doc prep and ingestion pipeline across the board. What we're finding as we work with enterprise customers: 1. Attribution is super important, and VLMs are there yet. Combining them with layout analysis makes for a winning combo. 2. VLMs are great at prompt-based extraction, but if you have document automation and you don't know where in tables you'll be searching or need to reproduce faithfully -- then precise table extraction is important. 3. VLMs will continue to get better, but the price points are a result of economies of scale that document parsing vendors don't get. On the flip side, document parsing vendors have deployment models that Gemini can't reach.
• seunosewa 2y ago
  He found the one thing that Gemini does better.
• WikiFuck 2y ago
  [dead]
• ady9999 2y ago
  We have been building smaller and more efficient VLMs for document extraction from way before and we are 10x faster than unstructured,reducto (the ocr vendors) with an accuracy of 90%. P.S. - You can find us here (unsiloed-ai.com) or you can reach out to me on adnan.abbas@unsiloed-ai.com
• BenGosub 2y ago
  They do not test Llamaparse on the accuracy benchmark. In my personal experience Llamaparse was one of the rare tools that always got the right information. Also, the accuracy is only based on tables and we had issues with irregular text structures as well. It is also worth noting that when using an LLM, a non-deterministic tool to do something deterministic is a bit risky and you need to write, modify and maintain a prompt.
• aravart 2y ago
  Does anyone have some fleshed out source code, prompts and all, to try this on Gemini 2.0?
  • nbbaier 2y ago
    Also really interested in this
• uri_merhav 2y ago
  Gemini Flash 2.0 is impressive but it hardly captures all of the information in the PDF. It's great for getting vibes from the document or finding overall information in it. If you ask it to e.g. enumerate every line item from multiple tables in a long PDF it still falls flat (dropping some line items or entire sections etc). DocuPanda and to a lesser extent Unstrucutred handle this.
• amai 2y ago
  Better have a look at - https://mathpix.com/ https://mathpix.com/ - Docling : https://ds4sd.github.io/docling/ https://ds4sd.github.io/docling/
• killer-Xbox 2y ago
  Hi
• mansourdamanpak 2y ago
  I've built a simple OCR tool with Gemini 2 flash you can test it here :gemini2flashocr.netlify.app
• mansourdaman 2y ago
  I've built a simple OCR tool with gemini 2 flash with several options: 1-Simple OCR: Extracts all detected text from uploaded files 2-Advanced OCR: Enables rule-based extraction (e.g., table data) 3-Bulk OCR: Designed for processing multiple files at once The project will be open-source next week. You can try the tool here: https://gemini2flashocr.netlify.app https://gemini2flashocr.netlify.app
• jonesn11 2y ago
  OCR makes sense, but it is another asking for a summary. It is not there yet, gave a lot of incorrect details.
• tomasello77 2y ago
  I tried using Gemini 2.0 Flash for PDF-to-Markdown parsing of scientific papers after having good results with GPT-4o, but the experience was terrible. When I sent images of PDF page with extracted text, Gemini mixed headlines with body text, parsed tables incorrectly, and sometimes split tables—placing one part at the top of the page and the rest at the bottom. It also added random numbers (like inserting an “8” for no reason). When using the Gemini SDK to process full PDFs, Gemini 1.5 could handle them, but Gemini 2.0 only processed the first page. Worse, both versions completely ignored tables. Among the Gemini models, 1.5 Pro performed the best, reaching about 80% of GPT-4o’s accuracy with image parsing, but it still introduced numerous small errors. In conclusion, no Gemini model is reliable for PDF-to-Markdown parsing and beyond the hype - I still need to use GPT-4o.
• pmarreck 2y ago
  I have some out-of-print books that I want to convert into nice pdf's/epubs (like, reference-quality) 1) I don't mind destroying the binding to get the best quality. How do I do so? 2) I have a multipage double-sided scanner (fujitsu scansnap). would this be sufficient to do the scan portion? 3) Is there anything that determines the font of the book text and reproduces that somehow? and that deals with things like bold and italic and applies that either as markdown output or what have you? 4) how do you de-paginate the raw text to reflow into (say) an epub format that will paginate based on the output device specification?