OCR users face unpredictable accuracy, costly model fees, and censorship hurdles that hamper data extraction and compliance. ReconOCR unifies multiple OCR engines, reconciles outputs, flags inconsistencies, and enforces content‑policy safeguards—while delivering transparent, per‑page pricing.
Yet: how is pricing? Evaluating contents and routing appropriately isn't a new challenge in OCR, one of the oldest fields of applications in ML. Thus, how do the smaller open models perform in tandem with relatively pricy $/pg models & APIs? Your use case is remarkably rare relative to the volume and price sensitivity of enterprise data warehouse ops.
It depends on what you need. For example a while ago I scanned and OCR'ed a bunch of receipts to get a timeline of my salary. I only cared about the gross and net figures, and nothing else mattered. Tesseract's output had a bunch of errors and misdetections, but the main figures always came out OK, and a local LLM was able to pick them out from the noise every time.There's a big gulf between "it's as if a human being had transcribed it and reconstructed the original document" and "so completely broken it can't be used for anything".
The VLM's are so good at complex document understanding now. But you just can't trust them not to invisibly censor sensitive clinical/legal docs, even at the maximally permissive settings.And the deep learning OCR-only models won't censor, but can and do hallucinate. I've yet to see a 'scan with different approaches and reconcile and say you're not sure if they don't agree' system just work for generic complex documents.
> You should at least try to explain your contrarian positionFWIW I explained it fully and provided the argument, along with explicit grounding examples. Can you tell me what exactly you don't understand?To quote your other post:> This is the weakest part of the argument. Absolutely no reason to believe the second inventor will be 10x faster.Nor is there any reason to believe that it will work at any usable speed. An LLM capable of AGI running at 0.000001tok/s isn't a very good moat. Nor is it a good moat if it can't actually realize anything material fast enough. Lord knows America has labor
Not the person you’re responding to, but I’ve had Claude refuse to OCR pages from in-copyright books. I was sometimes (but not always) able to get around that by changing models, by telling it that I was doing the text conversion only for personal use, or by first telling it to use Tesseract or another OCR engine to do the initial pass and then having a Claude subagent proofread and clean up the OCR output.I’ve also had it refuse to OCR public-domain books that included content that it didn’t like, such as references to prostitution in 19th-century books about Japan.I had one session where Cla
I'm basically doing the OCR twice, except in the Claude proofreading pass, it is not being asked to transcribe the document to a Markdown file. I'm pointing it to the same image input files, and to the Mistral OCR transcript Markdown file (it knows it's a Mistral OCR output), and ask Claude to check that the text is correct and point out the errors - and then make the necessary edits.I can't speak for Mistral OCR 4.1, but the hallucinations in 4.0 were so egregious (just completely making up new sentences in the middle of a page) that I knew I can't trust Mistral OCR on its own.
Hmm, that's an interesting idea. But it's the classifier that is triggering, and it triggers specifically on Claude's output. So I think the /dev/random case wouldn't work, because that gets Claude into the state of just reproducing the entire text from the original again.It doesn't always get flagged. Single pages are almost always okay. Running a program that sequentially runs single pages through the API is often not okay - I wrote a program in the early 4.x days before the rule came in, that's how I hit it first. But I've also had entire articles go through just fine recently in a Claude C
"it refuse to OCR public-domain books that included content that it didn’t like, such as references to prostitution in 19th-century books about Japan."Thoughtcrime -like territory and self-sensorship. The AI safety lobby is such a vile influence on the freedom of expression and communication via technology (since AI is starting to eat up rest of technology).I guess the main problem is positioning AI tools as "human-equivalent" creators by the big AI corps. If they were positioned simply as "better OCR and proofreading" people would attribute to them as much responsibility as they would to a -
Recommended execution roadmap for "ReconOCR: AI-Driven OCR Reconciliation & Compliance"
Examine the 8 harvested raw posts to map specific feature complaints, workflow workarounds, and user friction points.
Build a minimalist solution focused exclusively on solving "OCR users face unpredictable accuracy, costly model fees, and censorship hurdles that hamper data extraction a..." without feature bloat.
Directly engage users in subreddits and developer forums who expressed frustration to offer early access beta invites.
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