End-to-End Chess Recognition

Athanasios Masouris, Jan van Gemert

2024

Abstract

Chess recognition is the task of extracting the chess piece configuration from a chessboard image. Current approaches use a pipeline of separate, independent, modules such as chessboard detection, square localization, and piece classification. Instead, we follow the deep learning philosophy and explore an end-to-end approach to directly predict the configuration from the image, thus avoiding the error accumulation of the sequential approaches and eliminating the need for intermediate annotations. Furthermore, we introduce a new dataset, Chess Recognition Dataset (ChessReD), that consists of 10,800 real photographs and their corresponding annotations. In contrast to existing datasets that are synthetically rendered and have only limited angles, ChessReD has photographs captured from various angles using smartphone cameras; a sensor choice made to ensure real-world applicability. Our approach in chess recognition on the introduced challenging benchmark dataset outperforms related approaches, successfully recognizing the chess pieces’ configuration in 15.26% of ChessReD’s test images. This accuracy may seem low, but it is ≈7x better than the current state-of-the-art and reflects the difficulty of the problem. The code and data are available through: https://github.com/ThanosM97/ end-to-end-chess-recognition.

Paper Citation

in Harvard Style

Masouris A. and van Gemert J. (2024). End-to-End Chess Recognition. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 393-403. DOI: 10.5220/0012370200003660

in Bibtex Style

@conference{visapp24,
author={Athanasios Masouris and Jan van Gemert},
title={End-to-End Chess Recognition},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={393-403},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012370200003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - End-to-End Chess Recognition
SN - 978-989-758-679-8
AU - Masouris A.
AU - van Gemert J.
PY - 2024
SP - 393
EP - 403
DO - 10.5220/0012370200003660
PB - SciTePress