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| A Text Extraction Method for Image with Complex Background Based on Conditional Random Field |
| LI Min-Hua1,2, WANG Chun-Heng1, XIAO Bai-Hua1, BAI-Meng2 |
1.Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
2.Department of Electrical and Information Engineering, Shandong University of Science and Technology Jinan, Jinan 250031 |
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Abstract Aiming at the problem of extracting text from images with complex background, a method based on conditional random field is proposed. The proposed method integrates various features and takes context information into account, thus it can extract text information effectively from images. The text extraction performance in different color spaces and with different features is compared. Experimental results demonstrate the validity of the proposed method.
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Received: 24 October 2008
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[1] Otsu N. A Threshold Selection Method from Gray-Level Histogram. IEEE Trans on System, Man and Cybernetics, 1977, 9(1): 62-66
[2] Niblack W. Introduction to Digital Image Processing. Upper Saddle River, USA: Prentice Hall, 1986: 115-116
[3] Sato T, Kanade T, Hughes E K, et al. Video OCR for Digital News Archive // Proc of the IEEE Workshop on Content-Based Access of Image and Video Database. Bombay, India, 1998: 52-60
[4] Song Yan, Liu Anan, Pang Lin, et al. A Novel Image Text Extraction Method Based on K-means Clustering // Proc of the 7th IEEE/ACIS International Conference on Computer and Information Science. Portland, USA, 2008: 185-190
[5] Gllavata J, Ewerth R, Stefi T, et al. Unsupervised Text Segmentation Using Color and Wavelet Features // Proc of the 3rd International Conference on Image and Video Retrieval. Dublin, Ireland, 2004: 216-224
[6] Gao Jiang, Yang Jie. An Adaptive Algorithm for Text Detection from Natural Scenes // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Hawaii, USA, 2001, Ⅱ: 84-89
[7] Chen Datong, Odobez J M, Bourlard H. Text Segmentation and Recognition in Complex Background Based on Markov Random Field // Proc of the 16th International Conference on Pattern Recognition. Québec, Canada, 2002, Ⅳ: 227-230
[8] Jung K, Kim K I, Jain K. Text Information Extraction in Images and Video: A Survey. Pattern Recognition, 2004, 37(5): 977-997
[9] Lafferty J D, McCallum A, Pereira F C N. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data // Proc of the 18th International Conference on Machine Learning. Williamstown, USA, 2001: 282-289
[10] Wang Yang, Loe K F, Wu J K. A Dynamic Conditional Random Field Model for Foreground and Shadow Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2006, 28(2): 279-289
[11] He Xuming, Zemel R S, Carreira-Perpinan M A. Multiscale Conditional Random Fields for Image Labeling // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA, 2005, Ⅱ: 695-702
[12] Kumar S, Heber M. Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification // Proc of the 9th IEEE International Conference on Computer Vision. Nice, France, 2003, Ⅱ: 1150-1157
[13] Reynolds J, Murphy K. Figure-Ground Segmentation Using a Hierarchical Conditional Random Field // Proc of the 4th Canadian Conference on Computer and Robot Vision. Québec, Canada, 2007: 175-182
[14] Zhong P, Wang Runsheng. A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images. IEEE Trans on Geoscience and Remote Sensing, 2007, 45(12): 3978-3988
[15] Quattoni A, Collins M, Darrel T. Conditional Random Fields for Object Recognition // Proc of the 18th Annual Conference on Neural Information Processing Systems. Vancouver, Canada, 2004: 1097-1104
[16] Vishwanathan S V N, Schraudolph N N, Schmidt M W. Accelerated Training of Conditional Random Fields with Stochastic Gradient Method // Proc of the 23rd International Conference on Machine Learning. Pittsburgh, USA, 2006: 969-976
[17] Weis Y. Comparing the Mean Field Method and Belief Propagation for Approximate Inference in MRF [EB/OL]. [2008-09-30]. http://www.cs.huji.ac.il/~yweiss/mfpaper.ps.gz
[18] Yedidia J S, Freeman W J, Weiss Y. Understanding Belief Propagation and Its Generalizations // Labemeyer G, Nebel B, eds. Exploring Artificial Intelligence in the New Millennium. San Francisco, USA: Morgan Kaufmann, 2003: 236-239 |
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2009, Vol. 22 
