Static Hand Gesture Recognition Using Novel Convolutional Neural Network and Support Vector Machine

Hand tracking and identification through visual means pose a challenging problem. To simplify the identification of hand gestures, some systems have incorporated position markers or colored bands, which are not ideal for controlling robots due to their inconvenience. The motion recognition problem can be solved by combining object identification, recognition, and tracking using image processing techniques. A wide variety of target detection and recognition image processing methods are available. This paper proposes novel CNN-based methods to create a user-free hand gesture detection system. The use of synthetic techniques is recommended to improve recognition accuracy. The proposed method offers several advantages over existing methods, including higher accuracy and real-time hand gesture recognition suitable for sign language recognition and human-computer interaction. The CNN automatically extracts high-level characteristics from the source picture, and the SVM is used to classify these features. This study employed a CNN to automatically extract traits from raw EMG images, which is different from conventional feature extractors. The SVM classifier then determines which hand gestures are being made. Our tests demonstrate that the proposed strategy achieves superior accuracy compared to using only CNN.