Zerox v1 – Document OCR with GPT-vision

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Hey everyone! Today we're launching the stable release of Zerox, our open source OCR tool we've been building at OmniAi. 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. In particular, we've seen the vision models do a great job on charts, infographics, and handwritten text. Documents are a visual format after all, so a vision model makes sense! I posted the first experiments on HN, and since then, we've had some great contributors who have helped turn this into a full package. We have two versions now: - pip package [ https://pypi.org/project/py-zerox/ ] - npm package [ https://www.npmjs.com/package/zerox ] Next steps for us are working on building an open source dataset for fine tuning. We've seen some early success with a charts=>markdown fine tuning data set, and excited to keep building. Github: https://github.com/getomni-ai/zerox You can try out a hosted version here: https://getomni.ai/ocr-demo

Visit product View on Hacker NewsOct 17, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, visual · Missing: mac, agents, macos

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, io · Missing: https docs, just released, exist

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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