Chess recognition refers to the task of identifying the chess pieces
configuration from a chessboard image. Contrary to the predominant approach
that aims to solve this task through the pipeline of chessboard detection,
square localization, and piece classification, we rely on the power of deep
learning models and introduce two novel methodologies to circumvent this
pipeline and directly predict the chessboard configuration from the entire
image. In doing so, we avoid the inherent error accumulation of the sequential
approaches and the need for intermediate annotations. Furthermore, we introduce
a new dataset, Chess Recognition Dataset (ChessReD), specifically designed for
chess recognition that consists of 10,800 images and their corresponding
annotations. In contrast to existing synthetic datasets with limited angles,
this dataset comprises a diverse collection of real images of chess formations
captured from various angles using smartphone cameras; a sensor choice made to
ensure real-world applicability. We use this dataset to both train our model
and evaluate and compare its performance to that of the current
state-of-the-art. Our approach in chess recognition on this new benchmark
dataset outperforms related approaches, achieving a board recognition accuracy
of 15.26% ($\approx$7x better than the current state-of-the-art).

通过深度学习模型和引入两种新方法，绕过棋盘检测、方格定位和棋子分类的流程，直接从整个图像预测棋盘配置。同时，引入一种新的数据集 ChessReD，用于训练模型并评估比较其性能，相较于现有的方法，该方法在新的基准数据集上实现了 15.26% 的棋盘识别精度（约为当前最先进方法的 7 倍）。