Deep Convolutional Network with Long Short-Term Memory Layers for Dynamic Gesture Recognition

The framework based on a convolutional neural network (CNN) with adding long short-term memory layers (LSTM) for recognizing hand gestures from a video stream in real-time is presented. A review and analysis of existing models relating to gesture recognition in deep learning are considered. The task was to perform hand gesture classification using deep convolutional neural networks and obtain a simple, precise and resource-efficient system for visual recognition of letters and digits in sign language. The model is stable to rather wide angles of hand rotation and independent of lighting due to the using of contour patterns. In experiments with CNN, 98.46% accuracy on the test dataset has been obtained.