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| Binarization for Document Image Based on Multi-scale Conditional Random Fields |
| Liu Kun Lv Xueqiang Wang Tao Shi Shuicai |
(Chinese Information Processing Research Center, Beijing Information Science &Technology University, Beijing 100101,China)
(Beijing TRS Information Technology Co.Ltd., Beijing 100101, China) |
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Abstract This paper proposes a new algorithm based on multi-scale conditional random fields. This algorithm treats the binarization as a tagging process, using mCRF to label every pixel in the image, so as to realize the binarization of the full image. MCRF of discriminate model can accommodate any of the non-independent features, which makes full use of information in the image. From the result can see this algorithm is better than common threshold method in effect.
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Received: 21 November 2008
Published: 25 April 2009
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Corresponding Authors:
Liu Kun
E-mail: liukun2007@yahoo.com.cn
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| About author:: Liu Kun,Lv Xueqiang,Wang Tao,Shi Shuicai |
| ［1］ 陈丹，张蜂，贺贵明. 一种改进的文本图像二值化算法 ［J］. 计算机工程, 2003, 29(13): 85-86.
［2］ He X, Zemel R, Carreira-perpinan M. Multiscale Conditional Random Fields for Image Labeling ［C］. In: IEEE Conference. Computer Vision and Pattern Recognition, 2004: 695-702.
［3］ Derin H, Elliott H. Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9:39-55.
［4］ Lafferty J, McCallum A, Pereira F. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data ［C］. In: ICML, 2001: 282-289.
［5］ Hinton G E. Training Products of Experts by Minimizing Contrastive Divergence ［J］. Neural Comp, 2002, 14(8): 1771-1800. |
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