Abstract
The historical documents are valuable cultural heritages and sources for the study of history, social aspect and life at that time. The digitalization of historical documents aims to provide instant access to the archives for the researchers and the public, who had been endowed with limited chance due to maintenance reasons. However, most of these documents are not only written by hand in ancient Chinese characters, but also have complex page layouts. As a result, it is not easy to utilize conventional OCR(optical character recognition) system about historical documents even if OCR has received the most attention for several years as a key module in digitalization. We have been developing OCR-based digitalization system of historical documents for years. In this paper, we propose dedicated segmentation and rejection methods for OCR of Korean historical documents. Proposed recognition-based segmentation method uses geometric feature and context information with Viterbi algorithm. Rejection method uses Mahalanobis distance and posterior probability for solving out-of-class problem, especially. Some promising experimental results are reported.
Chapter PDF
Similar content being viewed by others
Keywords
- Linear Discriminant Analysis
- Mahalanobis Distance
- Chinese Character
- Historical Document
- Optical Character Recognition
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Hara, S.: OCR for CJK classical texts preliminary examination. In: Proc. Pacific Neighborhood Consortium(PNC) Annual Meeting, Taipei, Taiwan, pp. 11–17 (2000)
Lixin, Z., Ruwei, D.: Off-line handwritten Chinese characterrecognition with nonlinear pre-classification. In: Proc. Inc. Conf. On Multimodal Interfaces (ICMI 2000), pp. 473–479 (2000)
Kim, M.S., Jang, M.D., Choi, H.I., Rhee, T.H., Kim, J.H.: Digitalizing Scheme of Handwritten Hanja Historical Documents. In: Proc. Document Image Analysis of Libraries(DIAL 2004), Palo Alto, California, pp. 321–327 (2004)
Tung, C.H., Lee, H.J., Tsai, J.Y.: Multi-stage precandidate selection in handwritten Chinese character recognition system. Pattern Recognition 27(8), 1093–1102 (1994)
Tong, L.C., Tan, S.L.: Speeding up Chinese character recognition in an automatic document reading system. Pattern Recognition 31(11), 1601–1612 (1998)
Chen, Q., Zhen, L.: Word Segmentation in Handwritten Chinese Text Image Based on Component Clustering Techniques. In: Proc. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, vol. 1, pp. 435–440 (2002)
Zhao, S., Chi, Z., Shi, P., Yan, H.: Two-stage segmentation of unconstrained handwritten Chinese characters. Pattern Recognition 36, 145–156 (2003)
Tseng, Y.H., Lee, H.J.: Recognition-based handwritten Chinese character segmentation using a probabilistic Viterbi algorithm. Pattern Recognition Letters 20, 791–806 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, M.S., Cho, K.T., Kwag, H.K., Kim, J.H. (2004). Segmentation of Handwritten Characters for Digitalizing Korean Historical Documents. In: Marinai, S., Dengel, A.R. (eds) Document Analysis Systems VI. DAS 2004. Lecture Notes in Computer Science, vol 3163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28640-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-28640-0_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23060-1
Online ISBN: 978-3-540-28640-0
eBook Packages: Springer Book Archive