Zugwise: Domain-Aware Game Reconstruction for Digitizing Handwritten Chess Scoresheets

The authors report in this preprint: Digitizing handwritten chess scoresheets has traditionally been framed as a handwriting-recognition task combined with move legality checks. However, the authors argue that this misses silent errors where a misread yields a legal but incorrect move, corrupting the remainder of the game. To address this, the authors present Zugwise, which repurposes computer-chess machinery—such as quiescence search, static exchange evaluation, and pin detection—to evaluate whether a human could have plausibly written a given sequence. It incorporates an absurdity detector, backward search for tracing error symptoms, and a Needleman-Wunsch merge combining evidence from both players' sheets. Evaluated on tournament sections in an oracle replay, the system reduced silent errors from 16 to 1 and from 50 to 13. Two learned signals (CTC re-scoring and a Maia-style human-move prior) raised the correct fix's top-rank rate from 58.8% to 78.0%. The software runs entirely in the browser and is free, private, and offline-capable, accompanied by a three-way adjudication protocol.