Extract code from image snippets with Code OCR App

Hacker News

Hi folks, ever found an interesting code snippet online and ended up manually typing it all out? Frustrating indeed. So I decided to make the Code OCR App. You can now extract the code from any image snippet with just three clicks. 1- Upload your desired file 2- Crop and adjust the image to include only the desired text portion. 3- Once extracted, simply copy and start using in your project right away! The text extraction is currently powered by tesseract.js so there might be inaccuracies in the extracted text. There are few other limitations as well which I am working on for a fix. Check out the app and do let me know of your thoughts. Your feedbacks are highly appreciated.

Visit product View on Hacker NewsJul 31, 2023

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

72%72% 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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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