DUJE (Dicle University Journal of Engineering) 10:2 (2019) Page 561-568 Recognition of static hand gesture with using ANN and SVM Julius BAMWEND 1,* , Mehmet Siraç ÖZERDEM 2 1 Dicle Üniversitesi, Elektrik Elektronik Mühendisliği Bölümü, Diyarbakır, ORCID iD 0000-0002-6549-940X 2 Dicle Üniversitesi, Elektrik Elektronik Mühendisliği Bölümü, Diyarbakır, ORCID iD 0000-0002-9368-8902 Research Article ARTICLE INFO Article history: Received 23 May 2019 Revised 16 June 2019 Accepted 17 June 2019 Available online 19 June 2019 Keywords: Dynamic / Static Hand Gesture Recognition, Artificial Neural Network, Histogram of Oriented Gradient, Support Vector Machine ABSTRACT Hand gesture recognition is a relevant study topic for a reason that sometimes we may not be in position to communicate verbally. There is need to design Hand gesture recognition systems in order to help people adopt to nonverbal communication mainly sign language. However, there is no clue to understand the meaning of gesture through the computers directly. So this calls for definitions that generalize models in a computer. That is why the machine-learning approaches are implemented in recognition systems. There are generally two types of hand gestures recognition systems which researches have concentrated on. These include static and dynamic Hand gesture recognition systems. However, in building Hand gesture recognition systems, various machine learning approaches have been used. For implementing the proposed system, MS Kinect depth sensor was used as a hardware. The Kinect depth sensor is composed of an infrared camera. This is an advantage to the systems that are designed basing on the depth sensing because factors like color, clothing and background have less effect on the performance. So Kinect based depth sensor systems have a high accuracy and performance making them relevant and applicable in our daily lives. In this paper, we propose a static hand gesture recognition system in real time using two machine learning methods namely Support Vector Machine and Artificial Neural Networks. We use of the newly launched Microsoft Kinect sensor for image extraction. The sensor helps us to extract the hand images. We implement the system on a Matlab platform for reasons that Matlab is widely used by researchers in different fields and that can handle complex computations. In the training of the model, we collect a hundred depth-based Histogram of Oriented Gradient features per alphabet from the hand gesture images which we trained, tested and validated using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). From this dataset, we can generate the generalized gesture model for each alphabet image. For the proposed system, the classification with ANN proves a higher performance then SVM. Doi:10.24012/dumf.569357 * Corresponding author Julius BAMWENDA  hbamwenda@gmail.com Please cite this article in press as J. Bamwend, M.S. Ozerdem, “Recognition of Static Hand Gesture with using ANN and SVM”, DUJE, vol. 10, no.2, pp. 561 -568, June 2019. progress in technology and sensing devices have been developed hence mouse and keyboards are becoming irrelevant. There have been a lot of studies related with how humans can interact with computers. Technological development has created a number of fields of study such as Gesture Recognition, Image processing and many others. Gesture recognition has become a very important field of study. It provides the basis for body recognition. Many researchers have carried out studies mainly in Face Recognition (FR) and Hand Gesture Recognition (HGR). Introduction The use of computers has evolved rapidly in many fields namely leisure industrial, communication, and so on. We utilize those machines almost every time at work, home, school and almost in every field now. In any way, computers are part of us and we can’t do away with them. Currently we know that using a computer requires interacting with some devices like mouse and keyboard. People started to use the mouse and keyboard in 1980s as a way to communicate with computers. As we speak today, there is a high