Visual Symphony for Swift and Accurate Object Detection in Choreographed Deck of Cards
subject: Accuracy; Convolutional Neural Network (CNN); Deep learning techniques; Playing cards
description: The Convolutional Neural Network model used for playing card recognition and categorization, offering trustworthy data regarding the suits of playing cards hearts, diamonds, clubs and spades as well as the corresponding numerical or alphabetical values. The model is built on a sophisticated dataset that guarantees high levels of precision for nearly all sorts of graphical representations and playing card scenarios. A wide range of entertainment andgames bands canuse the CNN idea. As aresult, the CNN-trained model is an excellent alternative for many different kinds of applications, including virtual reality games and card game automation, due to its capacity to extract and retain complex features from card pictures for accurate object identification. As a result, this research has shown how crucial deep learning models like CNNs are for enhancing computervision systems' suitability for real-world scenarios requiring precise and quick identification of objects. As a result, the suggested CNN-based approach offers a great chance to enhance cardidentification system performance and promoteadvancements in memory and gaming technology. 2024 IEEE.
creator: Manjunatha Swamy C.; Arunraja A.; Raghavendra Babu T.M.; Kiran B.; Vinodha D.; BabuKumar S.
source: 2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024 - Proceedings
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835038386-7
format: Online
language: English
type: Conference paper
coverage: Manjunatha Swamy C., Christ University, Bengaluru, India; Arunraja A., Christ University, Bengaluru, India; Raghavendra Babu T.M., Dept. of CSE, PES college of Engineering, Mandya, India; Kiran B., Dept.of CSE, ATME College of Engineering, Mysuru, India; Vinodha D., Christ University, Bengaluru, India; BabuKumar S., Christ University, Bengaluru, India