This paper proposes a new part-based approach for skew estimation of document images. The proposed method first estimates skew angles on rather small areas, which are the local parts of characters, and subsequently determines the global skew angle by aggregating those local estimations. A local skew estimation on a part of a skewed character is performed by finding an identical part from prepared upright character images and calculating the angular difference. Specifically, a keypoint detector (e.g. SURF) is used to determine the local parts of characters, and once the parts are described as feature vectors, a nearest neighbor search is conducted in the instance database to identify the parts. Finally, a local skew estimation is acquired by calculating the difference of the dominant angles of brightness gradient of the parts. After the local skew estimation, the global skew angle is estimated by the majority voting of those local estimations, disregarding some noisy estimations. Our experiments have shown that the proposed method is more robust to short and sparse text lines and non-text backgrounds in document images compared to conventional methods.