Effective Strategies for Sign Language Recognition Using Machine Learning Techniques

In this survey paper, we explore the concepts of gestures and gesture recognition systems, their applications, and methodologies. We compare and contrast various tools and techniques for perceiving gestures, including machine learning algorithms, computer vision, and sensor data. We also analyze different data sources and acquisition techniques, and discuss the challenges associated with training a gesture recognition system.
The paper highlights the importance of hand movements in communication, which can be complex and involve multiple joints and muscles. However, there is a limited amount of training data available, making it challenging to create accurate models. Additionally, real-time processing is necessary for many applications, but this requires high-speed computing and processing power, which can be a challenge for some devices.
Despite these challenges, the paper concludes that gesture recognition systems have numerous applications in various fields, including healthcare, gaming, and robotics. By demystifying complex concepts using everyday language and engaging metaphors, we can better understand the essence of this survey paper without oversimplifying it. In summary, gesture recognition systems are a vital tool for interpreting and responding to human gestures accurately, with numerous applications in various fields.