Over-exposure happens often in daily-life photography due to the range of light far exceeding the capabilities of the limited dynamic range of current imaging sensors. Correcting overexposure aims to recover the fine details from the input. Most of the existing methods are based on manual image pixel manipulation, and therefore are often tedious and time-consuming. In this paper, we present the first convolutional neural network (CNN) capable of inferring the photo-realistic natural image for the single over-exposed photograph. To achieve this, we propose a simple and lightweight Over-Exposure Correction CNN, namely OEC-cnn, and construct a synthesized dataset that covers various scenes and exposure rates to facilitate training. By doing so, we effectively replace the manual fixing operations with an end-toend automatic correction process. Experiments on both synthesized and real-world datasets demonstrate that the proposed approach performs significantly better than existing methods and its simplicity and robustness make it a very useful tool for practical over-exposure correction. Our code and synthesized dataset will be made publicly available.