I built a web app to translate burned-in text on images

Hacker News

I built Magic Translate, a simple web app that allows you to translate text on images in over 100+ languages. Magic Translate is designed to work with images with burned-in text. All you need to do upload is your original image, and Magic Translate will do the rest. No design files required. This is the first launch, right now it only supports use on Desktop, but I'm looking to add a mobile experience soon. The primary use case I had in mind was for marketers who wish to localize their content, but don't necessarily have the design team nor the time to hand-translate their creative assets. This app is singularly focused on image translation, rather than an entire suite of design tools. It's free to try out, feedback and suggestions are much appreciated!

Visit product View on Hacker NewsAug 20, 2025

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Did not reach leaderboard

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

77%77% 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.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

62%62% 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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

15%15% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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