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Text extraction and document image segmentation using matched wavelets and MRF model
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DOI：10.1109/TIP.2007.900098.png)
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摘要
En 中文
In this paper, we have proposed a novel scheme for the extraction of textual areas of an image using globally matched wavelet filters. A clustering-based technique has been devised for estim ating globally matched wavelet filters using a collection of groundtruth images. We have extended our text extraction scheme for the segmentation of document images into text, background, and picture components (which include graphics and continuous tone images). Multiple, two-class Fisher classifiers have been used for this purpose. We also exploit contextual information by using a Markov random field formulation-based pixel labeling scheme for refinement of the segmentation results. Experimental results have established effectiveness of our approach.
Keyword:
alpha-expansion
document image
globally matched wavelets (GMWs)
matched wavelets
Markov random field (MRF)
scene image
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期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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