# Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model
## 1 Introduction  
The interaction between humans and computers has increased widely, while the domain is witnessing continuous development, with new methods derived and techniques discovered. Hand gesture recognition is one of the most advanced domains in which computer vision and artificial intelligence has helped to improve communication with deaf people but also to support gesture-based signaling systems [1; 2]. Subdomains of hand gesture recognition include sign language recognition [3; 4; 5], recognition of special signal language used in sports [6], human action recognition [7], pose and posture detection [8; 9], physical exercise monitoring [10], and controlling smart home/assisted living applications with hand gesture recognition [11]. Over the years, computer scientists have used different computation algorithms and methods to help solve our problems while easing our lives [12]. The use of hand gestures in different software applications has contributed towards improving computer and human interaction [13]. The progress of the gesture recognition systems plays a vital role in thedevelopment of computer and human interaction, and the use of hand gestures in various domains is growing more frequent. The application of the use of hand gestures can now be seen in games [14], virtual reality [15; 16] and augmented reality [17], assisted living [18; 19], cognitive development assessment [20], etc. The recent development of hand gesture recognition in different sectors has grabbed the attention of industry, too, for human-robot interaction in manufacturing [21; 22], and control of autonomous cars [23]. The main aim of this real-time hand gesture recognition application is to classify and recognize the gestures. Hand recognition is a technique in which we use different algorithms and concepts of various techniques, such as image processing and neural networks, to understand the movement of a hand [24]. In general, there are countless applications of hand gesture recognition. For example, for deaf people who cannot hear, we can communicate with their familiar sign language. There are many object detection algorithms that help to detect and determine what the gesture is that each algorithm targets. This paper explores a few algorithms and detects which algorithm is better than the others, providing better accuracy with fast and responsive results. To achieve this detection, You Only Look Once (YOLO) v3 and Single Shot Detector (SSD) algorithms were used to evaluate the structure and mechanism deduction of hand gesture recognition. YOLO is a convolutional neural network algorithm, which is highly efficient and works tremendously well for real-time object detection [25; 26]. A neural network not only helps in feature extraction, but it can also help us understand the meaning of gesture, and help to detect an object of interest. A similar approach was adopted in [27], but the main difference is that in [27] it was done through Light YOLO, which is completely different from YOLOv3. Light YOLO is preferred for applications built on RaspberryPi. It achieves a good frame per second (fps) rate as compared to YOLOv3. However, the accuracy of YOLOv3 is 30% better when compared to Light YOLO, and as the application is not focused on the Internet-of-Things (IoT) product, YOLOv3 is preferred. The main contributions of this study are summarized as follows:  
* A lightweight proposed model where there is no need to apply as much preprocessing which involves filtering, enhancement of images, etc. ;
* A labeled dataset in both Pascal VOC and YOLO format;
* This is the first gesture recognition model that is dedicated to the mentioned gestures using YOLOv3. We use YOLOv3 as it is faster, stronger, and more reliable compared to other deep learning models.