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You can turn a chess diagram in a screenshot, webpage, or ebook into a playable position by scanning the board, checking the recognized squares and game state, then opening or copying its FEN into an analysis board. Chessvision.ai documents scanning and editing workflows; the open-source fenshot project describes a browser-based route to Lichess. Recognition is not something to trust blindly: inspect the result before analyzing it.
Choose a scan route that fits the image
The right route depends on where the diagram is and how you want to use the result. These are documented workflows, not a controlled accuracy comparison.
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- Phone screenshot or gallery image: Chessvision.ai documents sharing or uploading images from mobile, then correcting and sharing the resulting position. Chessvision.ai
- Board on a webpage: Chessvision.ai documents browser-based scanning. The fenshot project also describes scanning screenshots in a browser and opening the result in Lichess analysis or the board editor. fenshot on GitHub
- Ebook or PDF diagram: Chessvision.ai documents features for scanning chess diagrams in ebooks. Check that the board is fully visible to the scanner. Chessvision.ai
For an existing screenshot, no physical chess equipment is needed; this is a digital conversion task.
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Scan, verify, and open the position
- Show the complete board. Avoid cropping any squares. Skewed or rotated diagrams may be harder to read; Chessvision.ai says its scanner can handle some distortion but recommends a straighter view when recognition is imperfect. Chessvision.ai
- Check orientation and every piece. Confirm which side is at the bottom, then compare the recognized piece on each square with the image. Arrows, highlights, unusual piece designs, clutter, multiple boards, and perspective distortion can complicate recognition.
- Set the game state. Verify whose turn it is and whether either side retains castling rights. These details are not reliably conveyed by the visible arrangement of pieces alone, so use the source position or accompanying notation when available.
- Correct any errors in an editor. Chessvision.ai documents manual editing of recognized positions. If the scanner does not offer the controls you need, use a board editor to place pieces and set the state, or enter the FEN manually.
- Export or hand off the checked position. Copy or share the FEN, or use a supported open-in option. Chessvision.ai documents FEN sharing and a Lichess handoff; fenshot describes opening its result in Lichess analysis or the board editor. Chessvision.ai · fenshot on GitHub
What to inspect before analysis
A scan represents the board position, not necessarily the entire history of the game. The key checks are:
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- Orientation: The board has not been read upside down or mirrored.
- Side to move: The next player matches the diagram’s intended position.
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Fenshot says its results include confidence information for individual tiles and that unreliable reads should be flagged. That is a project description, not an independently validated accuracy guarantee. Chessvision.ai advises users to inspect and edit results. fenshot on GitHub · Chessvision.ai
When recognition is wrong
Do not analyze a position just because the scanner produced a FEN. Correct the square or state in an editor, scan a flatter and less cluttered image, or rebuild the position manually in a board editor. A Lichess community forum thread titled “Feature Request: Convert an image to FEN using AI, and import to Board Editor” reflects this use case, but it is a forum request—not documentation that Lichess offers automatic image recognition as an official feature. Lichess forum thread
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Privacy and accuracy claims
Fenshot states that recognition runs client-side in the browser. The cited Chessvision.ai documentation does not make an equivalent claim about where image processing occurs, so do not assume the two handle images the same way. fenshot on GitHub · Chessvision.ai
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There is no independent, generalizable benchmark here for comparing scan accuracy. Fenshot reports “zero wrong tiles on every positive case” in its own real-screenshot evaluation set and says its legacy model misread up to 34 tiles per board. Those are project-reported results for its own evaluation, not a prediction of how either tool will perform on a particular image. fenshot on GitHub
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