Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIP.2012.2231089
Title: Robust document image binarization technique for degraded document images
Authors: Su, B.
Lu, S.
Tan, C.L. 
Keywords: Adaptive image contrast
degraded document image binarization
document analysis
document image processing
pixel classification
Issue Date: 2013
Source: Su, B.,Lu, S.,Tan, C.L. (2013). Robust document image binarization technique for degraded document images. IEEE Transactions on Image Processing 22 (4) : 1408-1417. ScholarBank@NUS Repository. https://doi.org/10.1109/TIP.2012.2231089
Abstract: Segmentation of text from badly degraded document images is a very challenging task due to the high inter/intra-variation between the document background and the foreground text of different document images. In this paper, we propose a novel document image binarization technique that addresses these issues by using adaptive image contrast. The adaptive image contrast is a combination of the local image contrast and the local image gradient that is tolerant to text and background variation caused by different types of document degradations. In the proposed technique, an adaptive contrast map is first constructed for an input degraded document image. The contrast map is then binarized and combined with Canny's edge map to identify the text stroke edge pixels. The document text is further segmented by a local threshold that is estimated based on the intensities of detected text stroke edge pixels within a local window. The proposed method is simple, robust, and involves minimum parameter tuning. It has been tested on three public datasets that are used in the recent document image binarization contest (DIBCO) 2009 & 2011 and handwritten-DIBCO 2010 and achieves accuracies of 93.5%, 87.8%, and 92.03%, respectively, that are significantly higher than or close to that of the best-performing methods reported in the three contests. Experiments on the Bickley diary dataset that consists of several challenging bad quality document images also show the superior performance of our proposed method, compared with other techniques. © 1992-2012 IEEE.
Source Title: IEEE Transactions on Image Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/39746
ISSN: 10577149
DOI: 10.1109/TIP.2012.2231089
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