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Detecting digital copyright violations on the internet
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Locality-sensitive hashing scheme based on p-stable distributions
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Skeleton-Based Recognition of Chinese Calligraphic Character Image
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Chinese calligraphy is the art of handwriting and is an important part of Chinese traditional culture. But due to the complexity of shape and styles of calligraphic characters, it is difficult for com-mon people to recognize them. So it would be great if a tool is provided to help users to recognize the unknown calligraphic characters. But the well-known OCR (Optical Character Recogni-tion) technology can hardly help people to recognize the unknown characters because of their deformation and complexity. Numerous collections of historical Chinese calligraphic works are digitized and stored in CADAL (China Academic Digital Associate Library) calligraphic system [1], and a huge database CCD (Calligraphic Character Dictionary) is built, which contains character images labeled with semantic meaning. In this paper, a LSH-based large scale Chinese calligraphic character recognition method is proposed basing on CCD. In our method, GIST descriptor is used to represent the global features of the calligraphic character images, LSH (Locality-sensitive hashing) is used to search CCD to find the similar character images to the recognized calligraphic character image. The recognition is based on the semantic probability which is computed according to the ranks of retrieved images and their distances to the recognized image in the Gist feature space. Our experiments show that our method is effective and efficient for recognizing Chinese calligraphic character image.