Open Access Open Access  Restricted Access Subscription or Fee Access

The New Otsu Thresholding for Binarization of the Ancient Copper Inscriptions


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecos.v11i10.10359

Abstract


This paper offers a new approach for the binarization of the Ancient Copper Inscription. The homogeneous color on this inscription requires a different method for the binarization process. This kind of document differs from documents on paper media written with ink because the characters on the inscription have been carved. The new Otsu thresholding combines the feature of Gray Level Co-occurrence Matrix (GLCM) and Otsu Thresholding. GLCM is used for the texture extraction of the image. The texture, consisting in a pattern of color intensity, is used to strengthen the difference between text inscription and inscription plate, to simplify the binarization process. Sliding window is used to take every part of the image to be extracted. Texture extraction process is conducted in CIE Lab color space using GLCM method. The result of texture feature extraction is converted into a grayscale image then binarized using Otsu method. By the evaluation of such binarization, the values are as follows: 95.4% for F-measure, 94.44% for Pseudo F-measure, 11.15 for PSNR and 37.82 for DRD. These values are better than all the comparison methods, demonstrating that Otsu method is suitable for being used.
Copyright © 2016 Praise Worthy Prize - All rights reserved.

Keywords


New Otsu Thresholding; Binarization; Texture; GLCM; Ancient Copper Inscription

Full Text:

PDF


References


K. Ntirogiannis, B. Gatos, and I. Pratikakis, “ICFHR2014 Competition on Handwritten Document Image Binarization (H-DIBCO 2014),” Proc. Int. Conf. Front. Handwrit. Recognition, ICFHR, vol. 2014–Decem, pp. 809–813, 2014.
http://dx.doi.org/10.1109/icfhr.2014.141

D. Ranganatha and G. Holi, “Hybrid binarization technique for degraded document images,” in Souvenir of the 2015 IEEE International Advance Computing Conference, IACC 2015, 2015, pp. 893–898.
http://dx.doi.org/10.1109/iadcc.2015.7154834

N. Otsu, “A Threshold Selection Method from Gray Level,” {IEEE} Trans. Syst. Man Cybern., vol. 9, no. 1, pp. 62–66, 1979.
http://dx.doi.org/10.1109/tsmc.1979.4310076

J. D. Yang, Y. S. Chen, and W. H. Hsu, “Adaptive thresholding algorithm and its hardware implementation,” Pattern Recognit. Lett., vol. 15, no. 2, pp. 141–150, 1994.
http://dx.doi.org/10.1016/0167-8655(94)90043-4

J. Sauvola and M. Pietikäinen, “Adaptive document image binarization,” Pattern Recognit., vol. 33, no. 2, pp. 225–236, 2000.
http://dx.doi.org/10.1016/s0031-3203(99)00055-2

J. Bernsen, “Dynamic Thresholding of Grey-Level Images.,” in Proceedings - International Conference on Pattern Recognition, 1986, pp. 1251–1255.
http://dx.doi.org/10.1016/0167-8655(91)90054-p

A. W. A. Arruda and C. A. B. Mello, “Binarization of Degraded Document Images Based on Combination of Contrast Images,” in Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, 2014, vol. 2014–Decem, no. 1, pp. 615–620.
http://dx.doi.org/10.1109/icfhr.2014.108

W. Niblack, An introduction to digital image processing. Prentice-Hall International, 1986.
http://dx.doi.org/10.1177/002072098001700324

B. Su, S. Lu, and C. L. Tan, “Robust document image binarization technique for degraded document images,” IEEE Trans. Image Process., vol. 22, no. 4, pp. 1408–1417, 2013.
http://dx.doi.org/10.1109/tip.2012.2231089

B. Gatos, I. Pratikakis, and S. J. Perantonis, “Adaptive degraded document image binarization,” Pattern Recognit., vol. 39, no. 3, pp. 317–327, 2006.
http://dx.doi.org/10.1016/j.patcog.2005.09.010

S. Das, S. Mandal, and A. K. Das, “Binarization of stone inscripted documents,” in 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS), 2015, pp. 11–16.
http://dx.doi.org/10.1109/cgvis.2015.7449883

I. Sreedevi, R. Pandey, N. Jayanthi, G. Bhola, and S. Chaudhury, “NGFICA Based Digitization of Historic Inscription Images,” Int. Sch. Res. Not., vol. 2013, no. 2013, 2013.
http://dx.doi.org/10.1155/2013/735857

S. T. Rasmana, Y. K. Suprapto, and K. E. Purnama, “A Study of Color Differences on the Metal Inscription Image Based on CIELab Color Space,” in Seminar on Intelegent Technology and Its Application (SITIA), 2013, pp. 326–331.
http://dx.doi.org/10.12928/telkomnika.v11i3.1135

T. Celik and T. Tjahjadi, “Multiscale texture classification using dual-tree complex wavelet transform,” Pattern Recognit. Lett., vol. 30, no. 3, pp. 331–339, Feb. 2009.
http://dx.doi.org/10.1016/j.patrec.2008.10.006

S. T. Rasmana, Y. K. Suprapto, I. K. E. Purnama, K. Uchimura, and G. Koutaki, “Texture Detection for Letter Carving Segmentation of Ancient Copper Inscriptions,” Int. J. Pattern Recognit. Artif. Intell., p. 1755002, 2016.
http://dx.doi.org/10.1142/s0218001417550023

A. Kaur and B. . Kranthi, “Comparison between YCbCr Color Space and CIELab Color Space for Skin Color Segmentation,” Int. J. Appl. Inf. Syst., vol. 3, no. 4, pp. 30–33, 2012.
http://dx.doi.org/10.1109/ngct.2015.7375244

E. Albuz, E. D. Kocalar, and a a Khokhar, “Quantized CIELab* space and encoded spatial structure for scalable indexing of large color image archives,” 2000 IEEE Int. Conf. Acoust. Speech Signal Process. Proc. Cat No00CH37100, vol. 6, pp. 1995–1998, 2000.
http://dx.doi.org/10.1109/icassp.2000.859223

S. Bansal and D. Aggarwal, “Color Image Segmentation using CIELab Color Space using Ant Colony Optimization,” Int. J. Comput. Appl., vol. 29, no. 9, pp. 28–34, 2011.
http://dx.doi.org/10.5120/3590-4978

R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man. Cybern., vol. 3, no. 6, pp. 610–621, 1973.
http://dx.doi.org/10.1109/tsmc.1973.4309314

C.-Y. Chang, S.-J. Chen, and M.-F. Tsai, “Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images,” Pattern Recognit., vol. 43, no. 10, pp. 3494–3506, Oct. 2010.
http://dx.doi.org/10.1016/j.patcog.2010.04.023

M. S. Hosseini and M. Zekri, “Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System,” J. Med. Signals Sensors, vol. 2, no. 1, pp. 49–60, 2012.
http://dx.doi.org/10.1109/iccima.2007.302

M. Radhakrishnan and T. Kuttiannan, “Comparative Analysis of Feature Extraction Methods for the Classification of Prostate Cancer from TRUS Medical Images,” IJCSI Int. J. Comput. Sci. Issues, vol. 9, no. 1, pp. 171–179, 2012.
http://dx.doi.org/10.1109/icprime.2012.6208367

X. Yang, S. Tridandapani, J. J. Beitler, D. S. Yu, E. J. Yoshida, W. J. Curran, and T. Liu, “Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity.,” Med. Phys., vol. 39, no. 9, pp. 5732–9, Sep. 2012.
http://dx.doi.org/10.1118/1.4747526

A. K. Mohanty, S. Beberta, and S. K. Lenka, “Classifying Benign and Malignant Mass using GLCM and GLRLM based Texture Features from Mammogram,” Int. J. Eng. Res. Appl., vol. 1, no. 3, pp. 687–693, 2011.
http://dx.doi.org/10.1007/s00521-012-1025-z

J. Lal Raheja, S. Kumar, and A. Chaudhary, “Fabric defect detection based on GLCM and Gabor filter: A comparison,” Opt. - Int. J. Light Electron Opt., vol. 124, no. 23, pp. 6469–6474, 2013.
http://dx.doi.org/10.1016/j.ijleo.2013.05.004

M. Gupta, D. Bhaskar, R. Bera, and S. Biswas, “Target detection of ISAR data by principal component transform on co-occurrence matrix,” Pattern Recognit. Lett., vol. 33, no. 13, pp. 1682–1688, Oct. 2012.
http://dx.doi.org/10.1016/j.patrec.2012.05.018

E. Ramos and D. S. Fernández, “Classification of leaf epidermis microphotographs using texture features,” Ecol. Inform., vol. 4, no. 3, pp. 177–181, 2009.
http://dx.doi.org/10.1016/j.ecoinf.2009.06.003

K. Ntirogiannis, B. Gatos, and I. Pratikakis, “Performance evaluation methodology for historical document image binarization,” IEEE Trans. Image Process., vol. 22, no. 2, pp. 595–609, 2013.
http://dx.doi.org/10.1109/tip.2012.2219550

H. Lu, A. C. Kot, and Y. Q. Shi, “Distance-Reciprocal Distortion Measure for Binary Document Images,” IEEE Signal Process. Lett., vol. 11, no. 2, pp. 228–231, Feb. 2004.
http://dx.doi.org/10.1109/lsp.2003.821748

Bazi, S., Nait Said, M., Extreme Learning Machines and Particle Swarm Optimization for Induction Motor Faults Detection and Classification, (2015) International Review of Electrical Engineering (IREE), 10 (4), pp. 501-509.
http://dx.doi.org/10.15866/iree.v10i4.7048

Haifeng, Y., Yong, Q., Liangqi, S., Gehao, S., Xiuchen, J., UHF Partial Discharge Pattern Recognition for GIS with Support Vector Machine, (2013) International Review of Electrical Engineering (IREE), 8 (1), pp. 491-496.

El Farissi, O., Moudden, A., Benkachcha, S., Recognition Improvement of Control Chart Pattern Using Artificial Neural Networks, (2015) International Review on Modelling and Simulations (IREMOS), 8 (2), pp. 227-231.
http://dx.doi.org/10.15866/iremos.v8i2.1946

Putov, V., Putov, A., Ignatiev, K., Kopichev, M., Asiedu-Baah, J., Mobile Manipulation Platform Control, (2014) International Review of Automatic Control (IREACO), 7 (4), pp. 412-419.

Perhinschi, M., Al-Sinbol, G., Artificial Dendritic Cell Algorithm for Advanced Power System Monitoring, (2016) International Review of Automatic Control (IREACO), 9 (5), pp. 330-340.
http://dx.doi.org/10.15866/ireaco.v9i5.10067

Jagadeesh, B., Kumar, P., Reddy, P., Fuzzy Inference System Based Robust Digital Image Watermarking in DWT-DCT Domain Using Human Visual System, (2016) International Review on Modelling and Simulations (IREMOS), 9 (4), pp. 265-270.

Dhahri, S., Zitouni, A., Torki, K., An Adaptive Motion Estimator Design for High Performances H.264/AVC Codec, (2013) International Review of Automatic Control (IREACO), 6 (2), pp. 221-227.

Velayudham, A., Kanthavel, R., Kumar, K., A Novel and Hybrid Optimization Mechanism For Denoising And Classification Of Medical Images using DTCWPT And Neuro-Fuzzy Classifiers, (2014) International Review on Computers and Software (IRECOS), 9 (3), pp. 513-525.

Sumathi, T., Karthikeyan, T., An Improved Identification System Using Iris Based on Curvelet Transform and WBCT, (2014) International Review on Computers and Software (IRECOS), 9 (8), pp. 1320-1327.
http://dx.doi.org/10.15866/irecos.v9i8.1745

Gattim, N., Rajesh, V., Multimodal Medical Image Fusion Under Redundant Transforms, (2015) International Review on Computers and Software (IRECOS), 10 (3), pp. 241-248.
http://dx.doi.org/10.15866/irecos.v10i3.4888


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize