Co

Convert Your PDF File to Text with No Servers

Hacker News

Convert Your PDF File to Text with No Servers

I recently discovered that - incredibly - PDFs that are just full of images are not OCR’d automatically on macOS If you have an image, the system-level “Live Text” feature will allow you to select and copy text. That’s really great. But if you have a PDF - you’re stuck. That’s part 1 of this project. Page 2 was seeing that most? all? web sites that offer pdf - ocr conversions require you to completely trust them as you need to upload your file to their server. That seems… not great. Part 3 was finding tesseract-wasm, an amazing project that I combined with the just as amazing pdf.js. That’s it! Thanks everyone Source code - https://github.com/gregsadetsky/pdf-to-ocr-but-no-servers

Visit product View on Hacker NewsOct 17, 2023

Actual performance

3points

Did not reach leaderboard

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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

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

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, code · Missing: agents, agent, cursor

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% 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

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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