Paper Title
Approach for Hand Gesture Recognition using CNN

Abstract
Gesture recognition is a popular issue in computer vision and pattern recognition, and it's crucial in a realistic human-computer interaction. Although significant progress has been achieved recently, quick and reliable hand gesture detection remains a challenge, since present approaches do not adequately manage performance and efficiency. Since there is no signal when a gesture begins and finishes in the video, executed movements should only be recognized once, and the entire architecture should be developed with memory and power budget in mind, real-time identification of dynamic hand gestures from video streams is a difficult and challenging. In this work combine approach is proposed based on convolution neural network using sliding window method. Proposed approach is evaluated based on different parameters like modality depth and modality RGB. Results are compared with other state of art methods and it is found that our proposed approach is better than previous method. Keywords - Hand Gesture Recognition, Convolution Neural Network, Sliding Window, Gesture Recognition