CMSOF: a structured data organization framework for scanned Chinese medicine books in digital libraries
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Organizing unstructured information from books into a well-defined structure is a significant challenge in digital libraries. Most digital libraries can provide only search services at the granularity of books and few libraries allow books to be accessed at the granularity of chapters, as manually constructing directory information for books is time-consuming. Extracting structured data from scanned books thus remains an urgent and important work. In this paper, we propose a novel structured data organization framework called CMSOF to organize scanned data automatically, and apply it to a Chinese medicine digital library. In the framework, image blocks and text blocks on the scanned page of books are separated based on the gray histogram projection method or a hybrid method of region growth and the Ada-Boosting classifier at first, and then the text structure is obtained from text blocks by text size and font type recognition. Finally, image blocks and structured OCRed text are correlated at the semantic level. By integrating the structured data into a Chinese medicine information system (CMIS), we can organize the Chinese medicine books well and users can access the books with flexibility, which indicates that CMSOF is an efficient framework to organize books mixed with images and text.
Key wordsDigital library Chinese medicine Structured data organization Cross media Image separation
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- Bosch, A., Zisserman, A., Munoz, X., 2007. Representing Shape with a Spatial Pyramid Kernel. Proc. CIVR, p.401–408. [doi:10.1145/1282280.1282340]Google Scholar
- Gatos, B., Danatsas, D., Pratikakis, I., Perantonis, S.J., 2005. Automatic table detection in document images. LNCS, 3686:609–618. [doi:10.1007/11551188_67]Google Scholar
- le Bourgeois, F., Trinh, E., Allier, B., Eglin, V., Emptoz, H., 2004. Document Images Analysis Solutions for Digital Libraries. Proc. 1st Int. Workshop on Document Image Analysis for Libraries, p.2–24. [doi:10.1109/DIAL.2004.1263233]Google Scholar
- Lu, X.N., Kahle, B., Wang, J.Z., Lee, C.G., 2008. A Metadata Generation System for Scanned Scientific Volumes. Joint Conf. on Digital Libraries, p.167–176. [doi:10.1145/1378889.1378918]Google Scholar
- Namboodiri, A.M., Jain, A.K., 2007. Document Structure and Layout Analysis. In: Digital Document Processing, p.29–48. [doi:10.1007/978-1-84628-726-8_2]Google Scholar
- Schapire, R.E., 1999. A Brief Introduction to Boosting. Proc. 16th Int. Joint Conf. on Artificial Intelligence, p.1401–1406.Google Scholar
- Shi, S.M., Wei, B.G., Yang, Y., 2009. Msuggest: a Semantic Recommender Framework for Traditional Chinese Medicine Book Search Engine. Conf. on Information and Knowledge Management, p.533–542. [doi:10.1145/1645953.1646022]Google Scholar
- Zhu, W.H., Wei, B.G., Zhuang, Y.T., Shi, S.M., Yang, Y., 2009. Content Integration in Digital Libraries. Proc. Int. Multimedia Conf. on Ambient Media Computing, p.57–64. [doi:10.1145/1631005.1631019]Google Scholar
- Zhu, Y., Tan, T., 2001. Font recognition based on global texture analysis. IEEE Trans. Pattern Anal. Mach. Intell., 223(10):1192–1200. [doi:10.1109/34.954608]Google Scholar