, , , , Anas Akkar Samuel Cregan Maame Araba Vander-Pallen Justin Cassens Tauheed Khan Mohd Department of Math and Computer Science, Augustana College, United States Corresponding Author: Dr. Tauheed Khan Mohd (tauheedkhanmohd@augustana.edu) Implementing Playing Cards BlackJack Game using OpenCV I. Abstract: Computer vision is a rapidly developing field that focuses on highly sophisticated picture analysis, manipulation, and comprehension. Its objective is to analyze what is happening in front of a camera and utilize that understanding to control a computer or robotic system or to present users with fresh visuals that are more enlightening or appealing than the original camera images. Computer vision technologies make it feasible for new user interfaces, augmented reality gaming, biometrics, automobile, photography, movie creation, Web search, and many more applications. This essay seeks to explain how computer vision can be utilized to play blackjack successfully. II. Introduction: Wouldn’t it be convenient to use your phone while playing Blackjack online and instantly know whether to Hit, Stand, or Double? Wouldn’t it also be convenient to use your phone during a Chess game and instantly know your best move? You could use the internet or simulations to help yourself in these situations. However, what if we could use our smartphone camera and have it detect the best next move for either Chess or Blackjack? That would be a more optimal solution, and it is possible using OpenCV. OpenCV (Open Source Computer Vision Library) is a computer vision and machine learning software library built to give a structure for computer vision applications. We can use this library to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high-resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements and recognize scenery… OpenCV has provided many APIs and was made compatible with mostly every device. It has C++, Python, Java, and MATLAB interfaces and supports Windows, Linux, Android, and Mac OS. III. Literature Review OpenCV began as a research project at Intel in 1998 and has been publicly available since 2000. 5 It provides programmers with tools to use computer vision to develop further programs. These tools are a “mix of low-level image-processing functions and high-level algorithms such as face detection, pedestrian detection, feature matching, and tracking.” 10 Using