Robust detection of hand gestures has remained a challenge due to background clutter encountered in real-world environments. In this work, a two-stage deep learning based approach is presented to detect hands robustly in unconstrained scenarios. We evaluate two recently proposed object detection techniques to initially locate hands in the input images. To further enhance the output of the hand detector we propose a convolutional neural network (CNN) based skin detection technique which reduces occurrences of false positives significantly. We show qualitative and quantitative results of the proposed hand detection algorithm on several public datasets including Oxford, 5-signer and EgoHands dataset. As a case study, we also report hand detection results robust to clutter on a proposed dataset of Indian classical dance (ICD) images. © 2017 IEEE.