Interactive Image Processing: Design of an AI Assisted Human Computer Interaction Logic Using Gesture Recognition Model Based on Logical Open CV Scheme

One method that can recognize hand gestures in real-time video is hand gesture recognition. A specific field of study is used to categorize the hand gesture. One of the difficult tasks in this study involving two main problems is the design of the hand gesture recognition system. The first step is to identify the hand. Making a sign that works for just one hand at a time is another challenge. The focus of this system is on developing a computer vision system that can detect, identify, and understand hand gestures, even when faced with tough circumstances such as variations in scale, orientation, position, and attitude. A variety of gestures, including numerical and sign language ones, must be implemented into this system for the development of this study endeavour to be successful. There are a lot of potential uses for this field, which provides a more organic and easy method for humans to interact with robots and system interfaces without the usage of additional hardware. Therefore, developing systems that can recognize individual human gestures and utilize them to transmit data or operate devices is the major objective of gesture recognition research as it pertains to Human-Computer Interaction (HCI). This necessitates real-time gesture identification and lightning-fast, very robust hand detection for vision-based hand gesture interfaces. Using an AI algorithm, this study introduces a solution for real-time gesture detection called the Logical Gesture detection Model (LGRM). It is general enough to be applied in many different kinds of human-computer interactions. To verify that the suggested model is effective, it is cross-validated with the traditional method known as the Cascading Gesture Classification Model (CGCM).