ChessQueries: Toward Better Chess Board Recognition

• URL: http://arxiv.org/abs/2608.30762v1
• Date: Mon, 31 Aug 2026 13:26:52 GMT
• Title: ChessQueries: Toward Better Chess Board Recognition
• Abstract summary: Chess board recognition is the task of mapping the image of a chess board to the information of which piece is on which square.<n>We introduce ChessQueries, a new method combining a ViT encoder with a DETR-style decoder, which outperforms existing methods.<n>Our method saturates the task on the two datasets, with an average 0.01 wrong squares per board (vs. SotA: 3.4 / 0.15 respectively)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Chess board recognition is the task of mapping the image of a chess board to the information of which piece is on which square. So far this task has two established benchmarks: ChessCog is synthetic, and ChessReD comes from smartphone pictures of a single chess board setup. We introduce ChessQueries, a new method combining a ViT encoder with a DETR-style decoder, which outperforms existing methods. On the ChessReD benchmark, we improve the state of the art from 15.3% to 99.2%, and demonstrate strong capabilities on out-of-distribution datasets. Our method saturates the task on the two datasets, with an average 0.01 wrong squares per board (vs. SotA: 3.4 / 0.15 respectively). We also share a new, harder public dataset, parsed from broadcasted top-level chess tournaments. Code, model weights and the SLCC data will be released.

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