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Automating the production of 2D hand-drawn animations is a significant and interesting component in computer graphics and vision. However, traditional methods in animation production pipeline always use physically or geometrically based models which are consuming due to complicated and massive computations, reducing their practicability. In this paper, we propose an efficient data-driven approach to create hand-drawn animations in an automatic manner. The key idea is to employ a correspondence match-based random search process to extract the geometry samples and the global motion pattern in an input animation sequence and then to generate a new output sequence through a coarse-to-fine sample-based synthesis algorithm. Our experiments demonstrate that our method achieves good results with high quality and performance, producing a range of artistic effects that previously required disparate and professional techniques.