Print Email Facebook Twitter End-to-End Chess Recognition Title End-to-End Chess Recognition Author Masouris, Thanos (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor van Gemert, J.C. (mentor) Verwer, S.E. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science Date 2023-08-31 Abstract 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% (≈7x better than the current state-of-the-art). Subject Chess recognitionChess datasetComputer visionDeep learning To reference this document use: http://resolver.tudelft.nl/uuid:5453c9dd-6a9b-4443-a4cf-c6b9db2f4c10 Part of collection Student theses Document type master thesis Rights © 2023 Thanos Masouris Files PDF chess_recognition_thesis_ ... souris.pdf 4.36 MB Close viewer /islandora/object/uuid:5453c9dd-6a9b-4443-a4cf-c6b9db2f4c10/datastream/OBJ/view