Towards the Processing of Historic Documents
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This chapter describes methods required for transforming complex document images into texts. The goal is to make the contents of those documents available for search engines, which are not born-digital but converted from a physical medium to a digital format. Established optical character recognition methods fail for documents for which no assumptions can be made regarding the, probably unknown, symbols contained in the document, historic documents being the example domain par excellence. This paper, however, has a much broader goal: it outlines fundamental problems as well as a methodology in the dealing with documents containing unknown and arbitrary symbols in order to provide a basis for discussions and future work within the digital library community. In particular, future advances will more closely require the interaction of researchers concerned with such diverse topics as document digitisation, reproduction, and preservation as well as search engines, cross-language processing, mobile libraries, and many further areas. Adopting a general view on the presented issues, researchers of the aforementioned areas should be sensitised for the problems met in processing complex, especially historic documents.
KeywordsDigital Library Document Image Shape Description Historic Document Document Processing
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- 2.Gottfried, B.: Shape from Positional-Contrast — Characterising Sketches with Qualitative Line Arrangements. DUV - Deutscher Universitätsverlag, Springer Science+Business Media, Wiesbaden (2007)Google Scholar
- 4.Ho, T.K.: Random decision forests. In: ICDAR 1995: Proceedings of the Third International Conference on Document Analysis and Recognition, p. 278. IEEE Computer Society Press, Washington, DC, USA (1995)Google Scholar
- 7.Meyer-Lerbs, L., Schuldt, A., Gottfried, B.: Glyph extraction from historic document images. In: Proceedings of the 2010 ACM Symposium on Document Engineering. ACM, New York (2010)Google Scholar
- 11.Shafait, F., Keysers, D., Breuel, T.M.: Efficient implementation of local adaptive thresholding techniques using integral images. In: Document Recognition and Retrieval XV, San Jose, CA, p. 6 (January 2008)Google Scholar