Integration of image processing algorithms for analysis of human-computer interactive behavior and detection of risk factors

This thesis deals with the integration of image processing and text detection libraries through the Python programming language for the purpose of identifying and analyzing human-induced risks for command-interactive devices. At the center of the study are text recognition and filtering processes over the source in video format, analytical evaluation of the data obtained as a result of these processes, and presentation of the results via e-mail in order to add value to the user. This systematic framework aims to identify and reduce potential risks associated with command-interactive devices. In the research, it was determined that the integration of image processing and text detection techniques into command recognition processes provides various advantages, such as increasing the success of command identification processes and detecting possible security risks in user interactive systems in advance. The integrated use of image processing and programming language libraries not only enables text extraction from video images, but also enables devices to respond more sensitively and effectively to user commands. This advanced methodology allows users to use devices more safely and effectively. Ultimately, this study reveals how image processing and text detection techniques can serve as a catalyst in the management of human-induced risks in command-interactive devices. The results obtained reveal that these techniques can provide significant improvements in the design and functionality of these devices and direct future research in this field.