Indonesian Journal of Electrical Engineering and Computer Science Vol. 21, No. 1, January 2021, pp. 242~252 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v21.i1.pp242-252  242 Journal homepage: http://ijeecs.iaescore.com Smart hand gestures recognition using K-NN based algorithm for video annotation purposes Malek Z.Alksasbeh 1 , Ahmad H. Al-Omari 2 , Bassam A. Y. Alqaralleh 3 , Tamer Abukhalil 4 , Anas Abukarki 5 , Ibrahim Alkore Alshalabi 6 , Amal Alkaseasbeh 7 1,3,4,5,6 Faculty of Information Technology, Al Hussein Bin Talal University, Ma'an, Jordan 2 Department of Computer Science, Faculty of Science, Norther Border University, Arar, Saudi Arabia 7 Ministry of Education and Higher Education, Al-Dawha, Qatar Article Info ABSTRACT Article history: Received Apr 10, 2020 Revised Jun 19, 2020 Accepted Jul 8, 2020 Sign languages are the most basic and natural form of languages which were used even before the evolution of spoken languages. These sign languages were developed using various sign "gestures" that are made using hand palm. Such gestures are called "hand gestures". Hand gestures are being widely used as an international assistive communication method for deaf people and many life aspects such as sports, traffic control and religious acts. However, the meanings of hand gestures vary among different civilization cultures. Therefore, because of the importance of understanding the meanings of hand gestures, this study presents a procedure whichcan translate such gestures into an annotated explanation. The proposed system implements image and video processing which are recently conceived as one of the most important technologies. The system initially, analyzes a classroom video as an input, and then extracts the vocabulary of twenty gestures. Various methods have been applied sequentially, namely: motion detection, RGB to HSV conversion, and noise removing using labeling algorithms. The extraction of hand parameters is determined by a K-NN algorithm to eventually determine the hand gesture and, hence showing their meanings. To estimate the performance of the proposed method, an experiment using a hand gesture database is performed. The results showed that the suggested method has an average recognition rate of 97%. Keywords: Hand gestures recognition K-NN Smart information systems Video anotation This is an open access article under the CC BY-SA license. Corresponding Author: Malek Zakarya Alksasbeh Faculty of Information Technology Al Hussein Bin Talal University, Ma'an, Jordan Email: malksabeh@ahu.edu.jo 1. INTRODUCTION The modern evolution of intelligent and smart applications in image processing technologies has provided significant services to the users. Normally, several human gestures are expressed by the body, head, face, arm, finger, and hands movement. However, hand gesture is the most famous and most commonly used assistive communication method. In addition, hand gestures have proven to be a robust means of communication to provide information [1]. Hand gesture recognition (HGR) is a challenging problem due to some important factors such as the unpredictable illumination conditions, uncontrolled environments, the relatively small size of the palm and the fingers, complexities of different signs, finger occlusions, and the overall complexity of the visual recognition of the hand gestures [2]. Therefore, our proposed system emphases on the recognition of sign words, which is a communications method using hands. This system helps in reducing the communication