A methodology for real-time ID card recognition is presented, utilizing a camera interface and a pre-trained machine learning model. The system initiates by opening the camera to continuously capture video frames, which undergo preprocessing to enhance quality and extract significant features. Techniques such as grayscale conversion, normalization, and noise filtering are applied to standardize the input frames, ensuring consistency with the training images. These preprocessing steps are crucial for minimizing errors and enhancing the model’s ability to recognize ID cards accurately. The processed frames are then analyzed by the machine learning model, which performs feature matching to identify the presence of ID cards. When a match is detected, the system provides immediate feedback through notifications, indicating successful identification or the absence of an ID card. This continuous monitoring process allows the system to adapt dynamically to changes in the camera’s field of view, maintaining high responsiveness and reliability in various environments. The integration of computer vision techniques with machine learning enables effective and scalable real-time ID card recognition, making this approach suitable for applications in security and access control.