Binarization Method Based on a Local Maximum-Ratio Threshold

Abstract:

For a vision-based analysis system, noise of the smear, the strain, or the badly illumination often causes serious errors in image analysis due to misclassification of object pixels and background pixels. An effective binarization method is thus required to improve the accuracy of segmentation and this paper proposes a novel algorithm for determining the threshold of each pixels. The proposed approach does not require statistical computation to determine the local threshold. The thresholding value can be easily determined by using the ratio of maximum intensity in a square region around each pixel. Experimental studies are conducted to evaluate the performance of the proposed method and the obtained results verify its segmentation abilities of hand-written text and music symbol under the badly illumination condition.

• Periodical: Advanced Materials Research (Volume 740)
• Pages: 338-344
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.740.338
• Online since: August 2013
• Authors: Jen Chao Tai, Bo Yi Chen
• Keywords: Binarization, Document Images, Image Processing, Local Threshold Based Algorithm

References

[1] B. Sankur and M. Sezgin: journal of Electronic Imaging, 146–165, (2004).

[2] S. S. Bukhari, F. Shafait and T. M Breuel: Journal of Universal Computer Science, vol. 15, no. 18, (2009).

[3] N. Otsu: IEEE Trans. System, Man, and Cybernetics, 62-66, (1978).

[4] E. Kavallieratou: Proceedings of 8th International Conference of Document Analysis and Recognition (ICDAR 2005), Vol. I, 463-467, Seoul, (2005).

[5] W. Bieniecki and S. Grabowski: Multi-pass approach to adaptive thresholding based image segmentation, Computer Engineering Dpt., CADSM'2005, February 23, 2005, Ukraine.

[6] J. Bernsen, in: Proc. Intl. Conf. on Pattern Recognition, 1251–1255, (1986).

[7] W. Niblack: (1986), An introduction to digital image processing, Prentice Hall, 115-116.

[8] J. Solis, K. Taniguchi, T. Ninomiya, T. Yamamoto and A. Takanishi, in: International Conference on Robotics and Automation, Japan, 2008, 3654–3659.

[9] J. C. Lin, H. H. Huang, Y. F. Li, J. C. Tai and L.W. Liu, in: Proc. of the 2010 International Symposium on Computer, Communication, Control and Automation (3CA), Taiwan, May 2010, 353–356.

[10] J. Cui, H. He and Y. Wang, in: IEEE 10th International Conference on Signal Processing (ICSP), China, Oct. 2010, 964–967.

[11] Information on http: /nazmialtun. blogspot. tw/2010/10/adaptive-local-thresholding-for. html.

[12] Information on http: /www. bitquill. net/blog/?tag=computer-science.

[13] Information on http: /www. comp. nus. edu. sg/~tancl/InformationMiningExtraction. htm.

[14] Information on http: /www. cs. bgu. ac. il/~isf.

[15] J. C. Tai and M. C. Lai: The 43rd Intl. Symp. on Robotics 2012, Taipei, Taiwan, Aug. 29–31, (2012).