A first-pass transcription is rarely the best the model can do. It reads the page once, commits to its best guess for every word, and hands you the result — including the words it half-saw and quietly resolved in the wrong direction. The fix isn’t a better model or a cleverer single prompt. It’s a second look: feed the draft back to the model alongside the same image and ask it to check its own work.
This “read-then-correct” loop reliably catches a share of errors that one pass leaves behind, and the technique generalizes well beyond handwriting. This guide is a deep-dive companion to the complete guide to transcribing handwritten scans; it assumes you already have a first-pass transcription in hand.
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