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Document image binarization using local features and Gaussian mixture modeling
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DOI：10.1016/j.imavis.2015.04.003.png)
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Abstract
En 中文
In this paper, we address the document image binarization problem with a three-stage procedure. First, possible stains and general document background information are removed from the image through a background removal stage. The remaining misclassified background and character pixels are then separated using a Local Co-occurrence Mapping, local contrast and a two-state Gaussian Mixture Model. Finally, some isolated misclassified components are removed by a morphology operator. The proposed scheme offers robust and fast performance, especially for both handwritten and printed documents, which compares favorably with other binarization methods. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Binarization
Handwritten documents
Historic documents
Classification
Background estimation
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