Chess piece recognition using deep convolutional neural networks

Orestis Papadimitriou; Athanasios Kanavos; Manolis Maragoudakis; Vassilis C. Gerogiannis

doi:10.1117/12.3030405

8 May 2024 Chess piece recognition using deep convolutional neural networks

Orestis Papadimitriou, Athanasios Kanavos, Manolis Maragoudakis, Vassilis C. Gerogiannis

Proceedings Volume 13162, Fourth Symposium on Pattern Recognition and Applications (SPRA 2023); 1316202 (2024) https://doi.org/10.1117/12.3030405
Event: Fourth Symposium on Pattern Recognition and Applications (SPRA2023), 2023, Napoli, Italy

Abstract

Chess piece recognition poses a significant challenge in computer vision due to the complex visual patterns and occlusions involved in identifying each piece’s type. In recent years, deep learning, particularly convolutional neural networks (CNNs), has emerged as a promising approach for image recognition, achieving state-of-the-art performance across various visual recognition tasks. In this paper, we propose a CNN-based approach for accurate chess piece recognition, capable of identifying the type of chess piece on each square of a chessboard. Our approach utilizes a deep neural network architecture that combines convolutional and fully connected layers to extract relevant features from chessboard images and make precise predictions. To evaluate our approach, we employ a large and diverse dataset of labeled chessboard images and compare its performance against state-of-the-art methods for chess piece recognition. Experimental results demonstrate that our approach surpasses existing methods, achieving an impressive accuracy of 98.9% on the test dataset.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Orestis Papadimitriou, Athanasios Kanavos, Manolis Maragoudakis, and Vassilis C. Gerogiannis "Chess piece recognition using deep convolutional neural networks", Proc. SPIE 13162, Fourth Symposium on Pattern Recognition and Applications (SPRA 2023), 1316202 (8 May 2024); https://doi.org/10.1117/12.3030405

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available