Free scanning app with ML crops, front/back pairing, and stitching

Hacker News

I built FrontBack Scanner to quickly digitize printed photos. It automatically detects and crops photos using ML, pairs front and back scans to preserve handwritten notes, and stitches oversized photos from multiple scans. Free Mac and Windows app. Everything runs locally, no account needed. Would love feedback from anyone tackling a box of family photos. This is the part of AI coding that excites me, niche software is cheap enough to create where it can be offered for free. AI should make ordinary life easier, not just make it easier to create another subscription.

Visit product View on Hacker NewsSep 8, 2026

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

66%66% 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 · Strong signals: mac, using, notes · Missing: agents, macos, agent

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

41%41% 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.

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

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

21%21% 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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