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This paper presents a textline detection method for degraded historical documents. Our method follows a conventional two-step procedure that the binarization is first performed and then the textlines are extracted from the binary image. In order to address the challenges in historical documents such as document degradation, structure noise, and skews, we develop new methods for the binarization and textline extraction. First, we improve the performance of binarization by detecting the non-text regions and processing only text regions. We also improve the textline detection method by extracting main textblock and compensating the skew angle and writing style. Experimental results show that the proposed method yields the state-of-the-art performance for several datasets.

Image registration is to find the correspondences between different images of the same scene. It still remains a challenging problem especially when there is dramatic changes of object appearances. This paper presents a new image registration method for alleviating this problem, which first finds sparse correspondences and interpolates continuous dense motions from them. Unlike conventional registration methods, we regularly place control points to prevent biased distribution of feature points and find optimized correspondences by minimizing the cost function. The cost function is based on SIFT descriptors and considers the smoothness of motion and topological relations. Especially, the topological term prevents inconsistent solution like fold-over or duplication artifacts. For the optimization, we adopt a dual-layer belief propagation and coarse-to-fine scheme. Based on barycentric coordinates, we finally estimate dense motion from sparse correspondences. Experimental results show that the proposed method yields more plausible results and is computationally efficient.