Playing Cards Classification and Detection Using Sequential CNN Model Through Machine Learning Techniques Using Artificial Intelligence

The task of identifying and classifying playing cards within images or video streams in computer vision involves employing a Sequential Convolutional Neural Network (CNN) model. This process combines image categorization and object recognition, presenting a sophisticated and engaging implementation of machine learning techniques. The significance of classifying and detecting playing cards extends to artificial intelligence and computer vision, with numerous practical applications. Systems for classifying and detecting playing cards utilizing artificial intelligence (AI) rely on computer vision methods and deep learning algorithms to achieve precise identification and categorization. These technological solutions not only enhance automation and efficiency across various tasks but also open up new possibilities for interaction and enjoyment in the domain of playing card games, making them accessible and engaging in diverse industries and user experiences. The primary objective of this research is to categorize playing cards and predict their expected accuracy, targeting a result of 75 percent. The high level of accuracy demonstrated in this study holds potential benefits for future researchers exploring this technology and subject matter.