Gestures are universal means of communication without any language barrier. Detecting gestures and recognition of its meaning are key steps for researchers in computer vision. Majority of the work is done in sign language already. Sign language datasets are compared with respect to their usability and diversity in terms of various signs. This paper highlights the available datasets from three dimensional body scans to hand action gestures. Their usability and strategies used to achieve the desired results are also discussed. Major neural networks are evaluated in terms of varied parameters and feutures. A Methodology for effective gesture recognition in real is proposed. Lastly Results achieved through an Open CV in combination with Sci-kit learn library based technique for gesture recognition are presented and analyzed in terms of efficacy and efficiency.