Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7223832 | Optik - International Journal for Light and Electron Optics | 2018 | 6 Pages |
Abstract
This paper presents a support vector machine (SVM) based method for degraded historical document image binarization. Given a degraded historical document image, the proposed method first segments the image into wâ¯Ãâ¯w regions and implements a local contrast enhancement in each image block. We then use a SVM to select an optimal global threshold for binarization of each image block. Finally, the entire image is further binarized by a locally adaptive thresholding method. The proposed method has been evaluated over the recent Document Image Binarization Competition (DIBCO) datasets. The experimental results show that our proposed method outperforms other state-of-the-art techniques in terms of F-measure, NRM, DRD, and MPM.
Related Topics
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Engineering (General)
Authors
Wei Xiong, Jingjing Xu, Zijie Xiong, Juan Wang, Min Liu,