Alternatives
Products that do what Open-weight OCR got so cheap I had to share it does
This was not supposed to become a product. When PaddleOCR-VL-1.6 dropped, independent benchmarks put it at the top of document parsing models. I had to try it. I needed a provider, but there simply isn't one ready for production that I would trust. So i set one up myself. I assumed that even after getting it running, serving a vision-language model would be expensive. It turns out the opposite is true. Once I had it running properly, the cost was absurdly low. At proper GPU utilization, the cost is only around $1 per 1,000 pages. The nearest competitors are either much lower quality (Azure…
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Vision models have been gaining popularity as a replacement for traditional OCR. Especially with Gemini 2.0 becoming cost competitive with the cloud platforms. We've been continuously evaluating different models since we released the Zerox package last year (https://github.com/getomni-ai/zerox). And we wanted to put some numbers behind it. So we’re open sourcing our internal OCR benchmark + evaluation datasets. Full writeup + data explorer here: https://getomni.ai/ocr-benchmark Github: https://github.com/getomni-ai/benchmark Huggingface:…
2025 · getomni.ai
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I built OCR Arena as a free playground for the community to compare leading foundation VLMs and open-source OCR models side-by-side. Upload any doc, measure accuracy, and (optionally) vote for the models on a public leaderboard. It currently has Gemini 3, dots.ocr, DeepSeek, GPT5, olmOCR 2, Qwen, and a few others. If there's any others you'd like included, let me know!
Nov 2025 · ocrarena.ai
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Last week was big for open source LLMs. We got: - Qwen 2.5 VL (72b and 32b) - Gemma-3 (27b) - DeepSeek-v3-0324 And a couple weeks ago we got the new mistral-ocr model. We updated our OCR benchmark to include the new models. We evaluated 1,000 documents for JSON extraction accuracy. Major takeaways: - Qwen 2.5 VL (72b and 32b) are by far the most impressive. Both landed right around 75% accuracy (equivalent to GPT-4o’s performance). Qwen 72b was only 0.4% above 32b. Within the margin of error. - Both Qwen models passed mistral-ocr (72.2%), which is specifically trained for OCR. - Gemma-3…
2025 · github.com
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I wanted to share our new speech to text model, and the library to use them effectively. We're a small startup (six people, sub-$100k monthly GPU budget) so I'm proud of the work the team has done to create streaming STT models with lower word-error rates than OpenAI's largest Whisper model. Admittedly Large v3 is a couple of years old, but we're near the top the HF OpenASR leaderboard, even up against Nvidia's Parakeet family. Anyway, I'd love to get feedback on the models and software, and hear about what people might build with it.
Feb 2026 · github.com
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Almost exactly 1 year ago, I submitted something to HN about using Llama2 (which had just come out) to improve the output of Tesseract OCR by correcting obvious OCR errors [0]. That was exciting at the time because OpenAI's API calls were still quite expensive for GPT4, and the cost of running it on a book-length PDF would just be prohibitive. In contrast, you could run Llama2 locally on a machine with just a CPU, and it would be extremely slow, but "free" if you had a spare machine lying around. Well, it's amazing how things have changed since then. Not only have models gotten a lot better,…
2024 · github.com
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This started out as a weekend hack with gpt-4-mini, using the very basic strategy of "just ask the ai to ocr the document". But this turned out to be better performing than our current implementation of Unstructured/Textract. At pretty much the same cost. I've tested almost every variant of document OCR over the past year, especially trying things like table / chart extraction. I've found the rules based extraction has always been lacking. Documents are meant to be a visual representation after all. With weird layouts, tables, charts, etc. Using a vision model just make sense! In…
2024 · github.com
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In the last 13 months I've spent total $1297 for Ahrefs subscription. Sounds like a little too much so I've build my own Keywords Research tool - Telescope. While building it my total bill was $51 for 2 months and 41k+ keywords found. Every page of keywords costs $0.03 - $0.05. 2 payment options - usage-based subscription or just top up your balance with the amount you'd like to. Telescope includes a couple of things: - Keywords Explorer - finding keywords based on seed phrase and filters - Keywords Ideas - keywords on interception on provided keywords - Ranked Keywords - keywords a domain…
2024 · withtelescope.com
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Hello HN! In a recent "Ask HN: What are you working on?" thread, I mentioned I was working on OCRing a large book: https://news.ycombinator.com/item?id=41971614 The post generated some interest so I thought I would keep HN posted. The book is Saint-Simon’s Memoirs -- an invaluable historical account of the French court under Louis XIV, full of wit, sharp observations, and of incredible literary value. I'm OCRing the edition of reference made between 1879-1930, that contains a lot of comments and footnotes: 45 volumes, ~27,000 pages. Here's a link to a blog post that describes…
2024 · blog.medusis.com
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1. Click capture text 2. Select an area on screen with text 3. Paste the text anywhere Are there other solutions out there? Yes, the best one that I've found is Text Sniper, it $8 so I decided to learn SwiftUI and release Text Capture for $0.99. This one uses MacOS builtin Vision API under the hood so it should also improve with new macOS releases. Would love to hear your feedback!
2024 · textcapture.app
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Hi HN, I’ve been working on an OCR pipeline specifically optimized for machine learning dataset preparation. It’s designed to process complex academic materials — including math formulas, tables, figures, and multilingual text — and output clean, structured formats like JSON and Markdown. Some features: • Multi-stage OCR combining DocLayout-YOLO, Google Vision, MathPix, and Gemini Pro Vision • Extracts and understands diagrams, tables, LaTeX-style math, and multilingual text (Japanese/Korean/English) • Highly tuned for ML training pipelines, including dataset generation and…
2025 · github.com
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OCR/Document extraction field has seen lot of action recently with releases like Mixtral OCR, Andrew Ng's agentic document processing etc. Also there are several benchmarks for OCR, however all testing for something slightly different which make good comparison of models very hard. To give an example, some models like mixtral-ocr only try to convert a document to markdown format. You have to use another LLM on top of it to get the final result. Some VLM’s directly give structured information like key fields from documents like invoices, but you have to either add business rules on top…
2025 · nanonets.com
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2021 · github.com
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For the uninitiated, a geocoder is maps-tech jargon for a search engine for addresses and points of interest. Geocoders are expensive to run. Like, really expensive. Like, $100+/month per instance expensive unless you go for a budget provider. I've been poking at this problem for about a month now and I think I've come up with something kind of cool. I'm calling it Airmail. Airmail's unique feature is that it can query against a remote index, e.g. on object storage or on a static site somewhere. This, along with low memory requirements mean it's about 10x cheaper to run an Airmail…
2024 · blog.ellenhp.me
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2022 · myfreeocr.com
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Try it out! https://glhf.chat/ Hey HN! We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant. Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and…
2024 · glhf.chat
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Built this because people working with large document sets had no free tool that handled batch processing cleanly. Tesseract is free and runs locally. For anything that needs more accuracy — Google Vision, Gemini, or any OpenRouter model — you bring your own API key. No subscription, no markup on your usage. Export as TXT, JSON, XML or PDF. AI engines support custom prompts so you can translate, extract form fields, or get structured output in one step. App: https://onlineocrfree.qzz.io Source: https://github.com/naimurhasan/online-ocr-free
Mar 2026 · onlineocrfree.qzz.io
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2024 · visionparser.com
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Hey, just a work related project I made, which could be open sourced :D If you're looking for an example on how to use/fine-tune yolov8, I feel like taking a look at this repo and reading the README could help you get up to speed (also linked some nice refs)! This is actually a full rewrite of a proprietary project I made (and documented on my site) like a year ago, will do some finishing touches (write blog post about it, mark the old version deprecated, record a tutorial on how to set it up on an Ubuntu server, etc, etc) in the following month, but felt like sharing it now, cuz I…
2023 · github.com
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