An efficient search method is desired for calligraphic characters due to the explosive growth of calligraphy works in digital libraries. However

traditional optical character recognition (OCR) and handwritten character recognition (HCR) technologies are not suitable for calligraphic character retrieval. In this paper

a novel shape descriptor called SC-HoG is proposed by integrating global and local features for more discriminability

where a gradient descent algorithm is used to learn the optimal combining parameter. Then two efficient methods

keypoint-based method and locality sensitive hashing (LSH) based method

are proposed to accelerate the retrieval by reducing the feature set and converting the feature set to a feature vector. Finally

a re-ranking method is described for practicability. The approach filters query-dissimilar characters using the LSH-based method to obtain candidates first

and then re-ranks the candidates using the keypoint- or sample-based method. Experimental results demonstrate that our approaches are effective and efficient for calligraphic character retrieval.