<abstract xmlns="http://www.w3.org/1999/xhtml">Chessboard and chess piece recognition is a computer vision problem that has not yet been efficiently solved. Digitization of a chess game state from a picture of a chessboard is a task typically performed by humans or with the aid of specialized chessboards and pieces. However, those solutions are neither easy nor convenient. To solve this problem, we propose a novel algorithm for digitizing chessboard configurations.We designed a method of chessboard recognition and pieces detection that is resistant to lighting conditions and the angle at which images are captured, and works correctly with numerous chessboard styles. Detecting the board and recognizing chess pieces are crucial steps of board state digitization.The algorithm achieves 95% accuracy (compared to 60% for the best alternative) for positioning the chessboard in an image, and almost 95% for chess pieces recognition. Furthermore, the sub-process of detecting straight lines and finding lattice points performs extraordinarily well, achieving over 99.5% accuracy (compared to the 74% for the best alternative).</abstract>

Chessboard and chess piece recognition is a computer vision problem that has not yet been efficiently solved. Digitization of a chess game state from a picture of a chessboard is a task typically performed by humans or with the aid of specialized chessboards and pieces. However, those solutions are neither easy nor convenient. To solve this problem, we propose a novel algorithm for digitizing chessboard configurations.We designed a method of chessboard recognition and pieces detection that is resistant to lighting conditions and the angle at which images are captured, and works correctly with numerous chessboard styles. Detecting the board and recognizing chess pieces are crucial steps of board state digitization.The algorithm achieves 95% accuracy (compared to 60% for the best alternative) for positioning the chessboard in an image, and almost 95% for chess pieces recognition. Furthermore, the sub-process of detecting straight lines and finding lattice points performs extraordinarily well, achieving over 99.5% accuracy (compared to the 74% for the best alternative).

Chessboard and Chess Piece Recognition With the Support of Neural Networks

European Center for Bioinformatics and Genomics

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Chessboard and Chess Piece Recognition With the Support of Neural Networks"}

Chessboard and chess piece recognition is a computer vision problem that has not yet been efficiently solved. Digitization of a chess game state from a picture...

Chessboard and Chess Piece Recognition With the Support of Neural Networks

Online veröffentlicht: 16. Dez. 2020

Seitenbereich: 257 - 280

Eingereicht: 30. Mai 2020

Akzeptiert: 12. Nov. 2020

DOI: https://doi.org/10.2478/fcds-2020-0014

Schlüsselwörter
chessboard detection, chess pieces recognition, pattern recognition, chess-board recognition, chess, neural networks

© 2020 Maciej A. Czyzewski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Chessboard and Chess Piece Recognition With the Support of Neural Networks

Online veröffentlicht: 16. Dez. 2020

Seitenbereich: 257 - 280

Eingereicht: 30. Mai 2020

Akzeptiert: 12. Nov. 2020

DOI: https://doi.org/10.2478/fcds-2020-0014

Schlüsselwörterchessboard detection, chess pieces recognition, pattern recognition, chess-board recognition, chess, neural networks

© 2020 Maciej A. Czyzewski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
chessboard detection, chess pieces recognition, pattern recognition, chess-board recognition, chess, neural networks