Content Based Search Engine for Historical Calligraphy Images

Paper collections of historical calligraphy objects in Libraries and museums are scanned into document images to serve the academic society. However, these digitized collections are in image format, lacking the technology to search by image content. This paper proposes a search engine for searching calligraphy image content. First, 2503 page images are segmented into characters and components. Second, characters are interactively labeled and features are extracted to build a calligraphy database. When an image search query is submitted, coarse features are first extracted and used to prune the long list of calligraphy characters into a shorter list. Then fine shape features are employed to determine the most similar characters. iDistance and NB-Tree are used to create the high dimensional index. The efficiency of the algorithm has been demonstrated through experiments with 110,737 individual calligraphic character images. This research provides a demonstration of the potential use of calligraphy content search on the web. Content Based Search Engine for Historical Calligraphy Images