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Hand gesture recognition plays a vital role in human computer interactions. Currently available systems for hand gesture recognition mainly concentrate on vision-based approaches and sensor-based approaches. These methods are vulnerable to environmental conditions such as light intensity, rain, smoke, and external noise, while suffering from privacy issues. On the contrary, RF sensing using radar is not as vulnerable to environmental conditions and does not significantly violate privacy. Through this project, a Doppler radar system with the directional sensing capability, operating at 2.45 GHz is developed to recognize hand gestures in real-time. The system can recognize seven hand gestures namely, single push, double push, swipe, swipe diagonal, swipe up-down, go away and no gesture. The classification is done using signal processing and machine learning techniques. We present a novel machine learning model with the use of a convolutional encoder-decoder and vision transformers for the classification. Group Members Gayangana Leelarathne Kavinda Kehelella Dhanuka Marasinghe Nisal Kariyawasam