Conference paper
A review of evaluation of optimal binarization technique for character segmentation in historical manuscripts
3rd International Conference on Knowledge Discovery and Data Mining (Phuket, 09/01/2010–10/01/2010)
2010
Abstract
A number of binarization techniques have been proposed in the past for automatic document processing. Although some studies have aimed to evaluate the performance of binarization algorithms, there is no automatic system that is capable of selecting the most appropriate method of binarization. While preprocessing techniques can be applied, binarization is essential to extract the objects in the first place before the characters can be separated for recognition. Although there are several commonly used binarization approaches, there is no single algorithm that is suitable for all images. Hence, there is a need to determine the optimal binarization algorithm for each image. The objective of this paper is to present a survey of the existing methods of binarization and evaluation measurement which have been developed recently. This will lead to the proposal and development of an approach for automatic selection of binarization techniques in handling historical document images.
Details
- Title
- A review of evaluation of optimal binarization technique for character segmentation in historical manuscripts
- Authors/Creators
- C.C. Fung (Author/Creator) - Murdoch UniversityR. Chamchong (Author/Creator) - Murdoch University
- Conference
- 3rd International Conference on Knowledge Discovery and Data Mining (Phuket, 09/01/2010–10/01/2010)
- Identifiers
- 991005544555607891
- Copyright
- © 2010 IEEE
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Conference paper
- Note
- Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This paper appears in: Third International Conference on Knowledge Discovery and Data Mining, 2010. WKDD '10. Phuket; 9 January 2010 through 10 January 2010, Article number 5432653, Pages 236-240.
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