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Extract Tables from Any Website – Images to JSON via OCR

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Extract Tables from Any Website – Images to JSON via OCR

built a two-step open-source tool that extracts tables from any website, even the hard ones that rely on dynamic rendering or CSS. Step 1: Capture tables as images using a headless browser Step 2: Run OCR to convert them into structured JSON This works well when traditional HTML parsers fail, like for complex styles, merged cells, or JS-rendered content. GitHub: https://github.com/enterpriseqa/extract_tables_from_websites Examples included. Feedback and contributions are welcome!

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, open · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% 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
34%34% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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