Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICPR.2010.780
Title: A self-training learning document binarization framework
Authors: Su, B.
Lu, S.
Lim Tan, C. 
Keywords: Document image binarization
Image pixel classification
Self-training learning framework
Issue Date: 2010
Source: Su, B.,Lu, S.,Lim Tan, C. (2010). A self-training learning document binarization framework. Proceedings - International Conference on Pattern Recognition : 3187-3190. ScholarBank@NUS Repository. https://doi.org/10.1109/ICPR.2010.780
Abstract: Document Image Binarization techniques have been studied for many years, and many practical binarization techniques have been developed and applied successfully on commercial document analysis systems. However, the current state-of-the-art methods, fail to produce good binarization results for many badly degraded document images. In this paper, we propose a self-training learning framework for document image binarization. Based on reported binarization methods, the proposed framework first divides document image pixels into three categories, namely, foreground pixels, background pixels and uncertain pixels. A classifier is then trained by learning from the document image pixels in the foreground and background categories. Finally, the uncertain pixels are classified using the learned pixel classifier. Extensive experiments have been conducted over the dataset that is used in the recent Document Image Binarization Contest(DIBCO) 2009. Experimental results show that our proposed framework significantly improves the performance of reported document image binarization methods. © 2010 IEEE.
Source Title: Proceedings - International Conference on Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/41767
ISBN: 9780769541099
ISSN: 10514651
DOI: 10.1109/ICPR.2010.780
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