Untorn

Untorn puts torn documents back together from one photo. Meta's SAM 3.1 cuts out every piece to the pixel, and Muse Spark reads the half-letters across each tear, like a crossword, to decide which pieces belong together. When a stack of pages is torn at once, the pieces share the very same edges and shape can't decide. Reading does.

A page torn by hand into 6 pieces, one photo from a phone, rebuilt live on untorn.vercel.app (waits cut; the run took 55 s).

Try it: untorn.vercel.app (free, no sign-up) · Demo video: youtu.be/XT53dzF6VU8 · Accuracy: untorn.vercel.app/accuracy · Verify it yourself: below

Meta Global AI Developer Hackathon 2026 · Indie Developer track · Theme: Push the Frontier · For judges: the four criteria

At a glance

• Real paper: a printed page torn by hand into 6 pieces came back whole in 5 of 5 live runs on the site, in 48–61 s, its transcript running on line by line across every tear. Counting our own runs of that page too: 7 of 8 with Muse Spark reading, as the site does today, and 12 of 15 across the four reader setups we tried (details).
• A published benchmark: on the ShredBench protocol (Findings of ACL 2026), Untorn makes 8× fewer text errors than Muse Spark alone on the same photos (normalised edit distance 0.050 against 0.404).
• Stacks: where pages were torn together and outlines repeat, Muse picked the right piece in 22 of 22 readings (chance: 1 in 3).
• Five Meta models, each with a measured job: SAM 3.1 finds and outlines every piece in one call; Muse Spark reads every seam, picks between identical outlines, turns pages upright, transcribes and translates; Muse Spark Open, Meta's open weights, answers the reads Muse Spark is slow on, and one switch sends every read to it (on the same tests it matched Muse Spark: 23 of 24 seams against 22, transcripts 2.4% against 2.6% character errors, 10.4 s a call against 15.5 s); Muse Wonder catalogues the rebuilt page for the archive (kind, date, place, sender, recipient, names), every field quoted from the page and checked against it; Muse Voice Transcribe writes down the archivist's spoken note of where the page was found. A run makes about one SAM 3.1 call and ten Muse Spark calls, and costs about $0.08–0.21 (measured on the live runs below); the record adds about $0.015 a page.
• Honest by design: two clues or no join. Pieces that don't fit are set aside, never forced; gaps in the transcript stay marked as gaps.

Built for the Meta Global AI Developer Hackathon (Oct 26 – Nov 4, 2026). Every line was written during the event, from the first commit on 26 Oct 2026 at 17:52 UTC (pre-existing vs new).

以上為 README 的開頭部分。其餘內容請閱讀原文。 檢視完整 README ↗