Piece Recognition

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Chessgame [1]

Piece Recognition, (Chess Board or Chess Position Recognition)
the ability of dedicated chess computers or chess playing robots to automatically recognize all the pieces on a chessboard, or in computer vision to convert an image of a real chessboard with pieces, or a chess diagram into a machine readable format specifying a chess position, such as Forsyth-Edwards Notation (FEN) or Extended Position Description (EPD).

Computer Vision

Piece recognition is an interesting topic in computer vision, machine learning and pattern recognition using one or more cameras along with digital image processing and object recognition, more recently supported by deep learning techniques as demonstrated by Daylen Yang with his Chess ID project [2], and by Richard Allbert with Vice [3].

Piece Recognition Boards

The user interface task to enter moves on a sensory board is often implemented with pressure sensitive or magnetic switches to determine origin and target squares with the implicit knowledge of the game state which piece was on the origin square and moved. The incremental update during game play starting from the initial position requires some care to keep internal and external board representation in sync, specially if analyzing with taking moves back. Here, real piece recognition offers not only much more comfort in entering arbitrary positions, but also more fault tolerant move recognition for dedicated units.

Piece recognition sensory boards require special electronics, and pieces with integrated passive components, such as piece type and piece color specific coils on ferrite core of a LC circuit. Selected via file- and rank multiplexer, the LC circuit forms a inductive coupled feedback loop of an amplifier forcing oscillation in piece type specific resonance, which could be measured or filtered, to detect the piece (if any) on the selected square. As reported by Robert Hyatt, Ken Thompson already had a piece recognition board based on coils in the base of the pieces, as demonstrated at ACM 1978 with Belle [4]. Along with Henry S. Baird, Ken Thompson further contributed to computer vision applied to reading chess a few years later [5].

Selected Systems

See also


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