A new scheme for unconstrained handwritten text-line segmentation
Pattern Recognition
A learning framework for the optimization and automation of document binarization methods
Computer Vision and Image Understanding
Historical document binarization based on phase information of images
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
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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.