ClearDoc – Extract fields from any document using OCR and LLM

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Hi HN! I recently launched a prototype of *ClearDoc*, an AI-powered tool to extract structured data from unstructured documents like invoices, bills of lading, certificates, etc. It uses *OCR (PaddleOCR)* and *LLMs* to detect and align key fields — even for complex documents with tables, nested fields, or in different languages. It doesn't require templates and can be *self-hosted* (demo runs on my own GPU). Live demo (no sign-up): http://cleardoc.v5ent.com/ Demo video: https://www.youtube.com/watch?v=u83T6iewfNs Right now: - Fields are auto-aligned visually on the document - Works with PDFs, images, scans - No custom field design/editing in the demo yet Would love feedback on: - Which use cases matter most to you? - What would make this valuable enough to adopt? Thanks!

Visit product View on Hacker NewsJul 28, 2025

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

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: lua, ide · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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