Preprocessing Model of Manuscripts in Javanese Characters

Anastasia Rita Widiarti¹, Agus Harjoko², Marsono  ³, Sri Hartati²

¹Department of Informatics Engineering, Sanata Dharma University, Yogyakarta, Indonesia.
²Department of Computer Science and Electronics, Gadjah Mada University, Yogyakarta, Indonesia.
³Department of Nusantara Literature, Gadjah Mada University, Yogyakarta, Indonesia.

DOI: 10.4236/jsip.2014.54014                

Abstract

Manuscript preprocessing is the earliest stage in transliteration process of manuscripts in Javanese scripts. Manuscript preprocessing stage is aimed to produce images of letters which form the manuscripts to be processed further in manuscript transliteration system. There are four main steps in manuscript preprocessing, which are manuscript binarization, noise reduction, line segmentation, and character segmentation for every line image produced by line segmentation. The result of the test on parts of PB.A57 manuscript which contains 291 character images, with 95% level of confidence concluded that the success percentage of preprocessing in producing Javanese character images ranged 85.9% - 94.82%.

Keywords

Binarization, Characters Segmentation, Line Segmentation, Noise Reduction, Manuscript Image Preprocessing

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Tangwongsan, S. and Sumetphong, C. (2008) Optical Character Recognition Techniques for Restoration of Thai Historical Documents. Proceeding of the International Conference on Computer and Electrical Engineering, Thailand, 20-22 December 2008, 531-535.
[2]	Surinta, O. and Chamchong, R. (2008) Image Segmentation of Historical Handwriting from Palm Leaf Manuscripts. In: Shi, Z.Z., Mercier-Laurent, E. and Leake, D., Eds., IFIP International Federation for Information Processing, Intelligent Information Processing IV, Vol. 288, Springer, Boston, 182-189. http://www.wbi.msu.ac.th/file/721/doc_57.pdf
[3]	Casey, R.G. and Licolinet, E. (1995) Strategies in Character Segmentation: A Survey. Proceeding of the Third International Conference on Document Analysis and Recognition (ICDAR), Canada, 14-16 August 1995, 1028-1033. http://perso.telecom-paristech.fr/~elc/papers/pami96.pdf
[4]	Garg, N.K., Kaur, L. and Jindal, M.K. (2010) Segmentation of Handwritten Hindi Text. International Journal of Computer Applications, 1, 19-23. http://www.ijcaonline.org/journal/number4/pxc387222.pdf
[5]	Lehal, G.S. and Singh, C. (2001) A Technique for Segmentation of Gurmukhi Text. Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, 2124, 191-200. http://advancedcentrepunjabi.org/pdf/A%20technique%20for%20segmentation%20of%20gurmukhi%20 text.pdf
[6]	Palakollu, S., Dhir, R. and Rani, R. (2011) A New Technique for Line Segmentation of Handwritten Hindi Text. Special Issue of International Journal of Computer Applications (0975-8887) on Electronics, Information and Communication Engineering—ICEICE, 5. http://research.ijcaonline.org/iceice/number5/iceice033.pdf
[7]	Widiarti, A.R. and Harjoko, A. (2006) Pengenalan Citra Dokumen Sastra Jawa (Studi Kasus Pada Buku Sastra Jawa: Menak Sorangan I dan Panji Sekar). SAINS dan SIBERNETIKA, 19, 269-282.
[8]	Zramdini, A. and Ingold, R. (1993) Optical Font Recognition from Projection Profiles. Electronic Publishing, 6, 249-260. http://cajun.cs.nott.ac.uk/compsci/epo/papers/volume6/issue3/ofr.pdf
[9]	Efstathiou, C.E. Signal Smoothing Algorithms. http://www.chem.uoa.gr/applets/appletsmooth/appl_smooth2.html
[10]	Behrend, T.E. (1990) Katalog Induk Naskah-Naskah Nusantara Jilid I Museum Sonobudaya Yogyakarta. Djambatan, Jakarta.
[11]	Gay, L.R. and Diehl, P.L. (1992) Research Methods for Business and Management. Mc. Millan Publishing Company, New York.
[12]	Wackerly, D.D., Mendenhall, W. and Scheaffer, R.L. (2008) Mathematical Statistics with Applications. 7th Edition, Thomson Learning, Inc., USA. http://fvela.files.wordpress.com/2012/01/mathematical_statistics_with_applications1.pdf