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A human gesture is a non-verbal form of communication and is critical in human-robot interactions. Vision-based gesture recognition methods play a key role to detect hand motion and support such interactions. Hand gesture recognition allows a convenient and usable interface between devices and users. Hand gestures can be used for various fields which makes it be able to be implemented for communication and further. Hand gesture recognition is not only useful for people who are hearingimpaired or disabled but also for the people who experienced a stroke, as they need to communicate with other people using different common essential gestures such as the sign of eating, drink, family and, more. In this paper, an approach for recognizing hand gesture based on Convolutional Neural Network (CNN) is proposed. The developed method is evaluated and compared between training and testing modes based on several metrics such as execution time, accuracy, sensitivity, specificity, positive and negative predictive value, likelihood and root mean square. Results show that testing accuracy is 99% using CNN and is an effective technique in extracting distinct features and classifying data.

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