Abstract
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.
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References
Flickner, M., Sawhney, W., Niblack, H., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by Image and Video Content: The QBIC System. Computer 28, 23–32 (1995)
Gottfried, B.: Shape from Positional-Contrast — Characterising Sketches with Qualitative Line Arrangements. DUV - Deutscher Universitätsverlag, Springer Science+Business Media, Wiesbaden (2007)
Gottfried, B.: Qualitative Similarity Measures - The Case of Two-Dimensional Outlines. Computer Vision and Image Understanding 110(1), 117–133 (2008)
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)
Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 8(2), 179–187 (1962)
Lee, J.-S.: Digital image smoothing and the sigma filter. Computer Vision, Graphics, and Image Processing 24(2), 255–269 (1983)
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)
Pletschacher, S.: A self-adaptive method for extraction of document-specific alphabets. In: ICDAR 2009: Proceedings of the 10th International Conference on Document Analysis and Recognition, pp. 656–660. IEEE Computer Society, Los Alamitos (2009)
Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recognition 33(2), 225–236 (2000)
Schuldt, A., Gottfried, B., Herzog, O.: Towards the visualisation of shape features the scope histogram. In: Freksa, C., Kohlhase, M., Schill, K. (eds.) KI 2006. LNCS (LNAI), vol. 4314, pp. 289–301. Springer, Heidelberg (2007)
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)
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Gottfried, B., Meyer-Lerbs, L. (2011). Towards the Processing of Historic Documents. In: Bernardi, R., Chambers, S., Gottfried, B., Segond, F., Zaihrayeu, I. (eds) Advanced Language Technologies for Digital Libraries. NLP4DL AT4DL 2009 2009. Lecture Notes in Computer Science, vol 6699. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23160-5_2
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DOI: https://doi.org/10.1007/978-3-642-23160-5_2
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