An efficient binarization method for ancient Mongolian document images

In order to recognize and retrieve the Mongolian Kanjur images, lots of preprocessing tasks should be done. In this paper, we concentrate on the binarization of the Mongolian Kanjur images and we have proposed an efficient binarization method for them. The proposed method is applied to each image as follows: First, some preprocessing tasks including grayscaling and smoothing are executed. Second, three well-known global thresholding methods are used for extracting regions of interest (ROIs) from every gray-level image. Then, each ROI is processed by a modified Sauvola's algorithm with variant sizes of the small windows. Experimental results have proved that the proposed binarization method is better than the original Sauvola's algorithm.

[1]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[2]  Ioannis Pratikakis,et al.  Efficient Binarization of Historical and Degraded Document Images , 2008, 2008 The Eighth IAPR International Workshop on Document Analysis Systems.

[3]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[4]  Nikos Papamarkos,et al.  Automatic Evaluation of Document Binarization Results , 2005, CIARP.

[5]  Guanglai Gao,et al.  Machine-Printed Traditional Mongolian Characters Recognition Using BP Neural Networks , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[6]  Øivind Due Trier,et al.  Evaluation of Binarization Methods for Document Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Chun Che Fung,et al.  Comparing background elimination approaches for processing of ancient Thai manuscipts on palm leaves , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[8]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[9]  Derek Bradley,et al.  Adaptive Thresholding using the Integral Image , 2007, J. Graph. Tools.

[10]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[11]  Matti Pietikäinen,et al.  Adaptive document image binarization , 2000, Pattern Recognit..

[12]  George D. C. Cavalcanti,et al.  A Heuristic Binarization Algorithm for Documents with Complex Background , 2006, 2006 International Conference on Image Processing.

[13]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[14]  Nikos Papamarkos,et al.  An evaluation survey of binarization algorithms on historical documents , 2008, 2008 19th International Conference on Pattern Recognition.

[15]  Adnan Amin,et al.  Automatic thresholding of gray-level using multistage approach , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[16]  B. Kapralos,et al.  I An Introduction to Digital Image Processing , 2022 .