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With advances in technology, robots play an important role in our lives. Nowadays, we have more chance to see robots service in our society such as intelligent robot for rescue and for service. Therefore, Human-Robot interaction becomes an essential issue for research. In this paper we introduce a combining method for hand sign recognition. Hand sign recognition is an essential way for Human-Robot Interaction (HRI). Sign language is the most intuitive and direct way to communication for impaired or disabled people. Through the hand or body gestures, the disabled can easily let caregiver or robot know what message they want to convey. In this paper, we propose a combining hands gesture recognition algorithm which combines two distinct recognizers. These two recognizers collectively determine the hand's sign via a process called CAR equation. These two recognizers are aimed to complement the ability of discrimination. To achieve this goal, one recognizer recognizes hand gesture by hand skeleton recognizer (HSR), and the other recognizer is based on support vector machines (SVM). In addition, the corresponding classifiers of SVM are trained using different features like local binary pattern (LBP) and raw data. Furthermore, the trained images are using Bosphorus Hand Database and in addition to taking by us. A set of rules including recognizer switching and combinatorial approach recognizer CAR equation is devised to synthesize the distinctive methods. We have successfully demonstrated gesture recognition experimentally with successful proof of concept.