Automatic Image Localization Pipeline

Hacker News

I’ve been experimenting with a side project that explores automating image localization. The pipeline takes an image containing text, detects text regions, removes the text, translates it, then re-renders it in a target language while attempting to preserve layout and visual balance. The goal is not perfect marketing copy or pixel-perfect DTP replacement. It is more about testing whether “good enough” localized visuals can be generated without manually recreating assets for each language. Some things I’ve been curious about while building this: • How different scripts behave (Latin vs CJK vs RTL) • Where layout preservation breaks down • Whether this is actually useful or just a novelty demo • What production constraints would make this impractical Examples: https://postimg.cc/gallery/1X04QFz If anyone here works with localization, design systems, or asset pipelines, I’d genuinely love to hear where you think this approach would fail.

Visit product View on Hacker NewsFeb 26, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual · 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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% 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

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

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