Optical Character Recognition for Brahmi Script Using Geometric Method

Authors

  • Neha Gautam Faculty of Computer Science and Information Technology, University Malaysia Sarawak.
  • Soo See Chai Faculty of Computer Science and Information Technology, University Malaysia Sarawak.

Keywords:

OCR, Brahmi Script, Geometric Features, Zone Method, Asian Scripts,

Abstract

Optical character recognition (OCR) system has been widely used for conversion of images of typed, handwritten or printed text into machine-encoded text (digital character). Previous researches on character recognition of South Asian scripts focus on modern scripts such as Sanskrit, Hindi, Tamil, Malayalam, and Sinhala etc. but little work is traceable to Brahmi script which is referred to as the origin of many scripts in south Asian. This study proposes a method for recognition of both handwritten and printed Brahmi characters which involve preprocessing, segmentation, feature extraction, and classification of Brahmi script characters. The geometric method was used for feature extraction into six different entities, followed by a newly developed classification rules to recognize the Brahmi characters based on the features. The method obtains accuracy of 91.69% and 89.55% for handwritten vowels and consonants character respectively and 93.30% and 94.90% for printed vowel and consonants character respectively.

References

C. V. Aravinda, and H. N. Prakash, “Kannada HandWritten Character Recognition by Edge Hinge and Edge Distribution Techniques Using Manhatan and Minimum Distance Classifiers,” World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering, vol. 2, no. 12, 2015.

D. Ghosh, T. Dube, and A. Shivaprasad, “Script Recognition-a review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 12, pp. 2142–2161, 2010.

S. Rawat, K. S. S. Kumar, M. Meshesha, I. D. Sikdar, A. Balasubramanian, and C. V. Jawahar, “ A semi-automatic adaptive OCR for digital libraries,” Lecture Notes in Computer Science, 3872 LNCS, pp. 13–24, 2006.

A. Soumya and G. H. Kumar, “Automatic Decipherment of Ancient Indian Epigraphical Scripts - A Brief Review,” International Journal of Computer Science and Emerging Technologies, vol. 2, no. 1, pp. 139–143, 2011.

Trautmann and R. Thomas, Languages and Nations The Dravidian Proof in Colonial Madras. Yoda Press, 2006.

U. Pal, R. Jayadevan, and N. Sharma, “Handwriting Recognition in Indian Regional Scripts: a survey of offline techniques,” ACM Transactions on Asian Language Information Processing, vol. 11, no. 1, pp. 1–35, 2012.

H. Winskel and P. Padakannaya, “South and Southeast Asian psycholinguistics,” Cambridge University Press, 2013.

A. Andreopoulos and J. K. Tsotsos, “50 Years of object recognition: Directions forward,” Computer Vision and Image Understanding, vol. 117, no. 8, pp. 827–891, 2013.

W. Wu and W. Yu, “Subpixel detection of circular objects using geometric property,” Proceedings of International Conference on Image, Signal and Vision Computing, pp. 236–240, 2009.

P. B. Pati and A. G. Ramakrishnan, OCR in Indian Scripts: A Survey. IETE Technical Review, vol. 22, no. 3, pp. 217-227, 2005.

D. Bandara, N. Warnajith, A. Minato, and S. Ozawa, “Creation of precise alphabet fonts of early Brahmi script from photographic data of ancient Sri Lankan inscriptions,” Canadian Journal on Artificial Intelligence, Machine Learning and Pattern Recognition, vol. 3, no. 3, pp. 33–39, 2012.

N. Warnajith, D. Bandara, S. B. Quarmal, M. Itaba, A. Minato, and S. Ozawa, “Computer Analysis of Photographic Data of Sri Lankan Early Brahmi Inscriptions,” Computer, vol. 3, no. 1, pp. 44–49, 2013.

G. Siromoney, R. Chandrasekaran, and M. Chandrasekaran, “Machine recognition of Brahmi script,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 4, pp. 648–654, 1983.

H. K. A. Devi, “Thinning: A Preprocessing Technique for an OCR System for the Brahmi Script,” Ancient Asia, vol. 1, no. 0, p. 167, 2006a.

H. K. A. Devi, “Thresholding: A Pixel-Level Image Processing Methodology Preprocessing Technique for an OCR System for the Brahmi Script,” Ancient Asia, 2006b.

N. Gautam, R. S. Sharma, and G. Hazrati, “Handwriting Recognition of Brahmi Script (an Artefact): Base of PALI Language,” Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems, Springer International Publishing, vol. 2, pp. 519-527, 2016.

D. D. Gaurav and R. Ramesh, “A feature extraction technique based on character geometry for character recognition,” arXiv preprint arXiv:1202.3884, 2012.

V. J. Dongre and V. H. Mankar, “Devnagari Handwritten Numeral Recognition using Geometric Features and Statistical Combination Classifier,” arXiv preprint arXiv:1310.5619, 2013.

M. U. Akram, Z. Bashir, A. Tariq, and S. A. Khan, “Geometric Feature Points Based Optical Character Recognition,” Industrial Electronics and Applications (ISIEA), IEEE Symposium on, pp. 86-89, Sep. 2013.

V. Dongre and V. Mankar, “Devanagari offline handwritten numeral and character recognition using multiple features and neural network classifier,” In Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on, pp. 425-431, 2015.

N. Assiwal and N. Sharma, A Geometric Feature Extraction Technique for Hindi Handwritten Character Recognition, 2016.

V. M. Aradhya, G. H. Kumar, and S. Noushath, S. “Multilingual OCR system for South Indian scripts and English documents: An approach based on Fourier transform and principal component analysis,” Engineering Applications of Artificial Intelligence, vol. 21, no. 4, pp. 658–668, 2008

P. S. P. Wang, Pattern Recognition, Machine Intelligence and Biometrics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

X. Li, Z. Wang, J. Xu, and J. Chen, “Research and application of a new thinning algorithm in car plate recognition system,” 4th International Conference on New Trends in Information Science and Service Science, 2010.

R. Jayadevan, S. R. Kolhe, P. M. Patil, and U. Pal, “Offline Recognition of Devanagari Script: A Survey,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 41, no. 6, pp. 782–796, 2011.

A. Lawgali, “A Survey on Arabic Character Recognition,” International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 8, no. 2, pp. 401-426, 2015.

S. Liu, Y. Liu, Z. Wang, and Y. Cao, “Automatic chessboard corner detection method,” IET Image Processing, vol. 10, no. 1, pp. 16–23, 2016.

S. Saha and N. D. Roy, “Automatic Detection of Bifurcation Points in Retinal Fundus-Images,” International Journal of Latest Research in Science and Technology, vol. 2, no. 2, pp. 105–108, 2013.

R. Szeliski, “Computer vision: algorithms and applications,” Springer Science and Business Media, 2010.

E. Cuevas, F. Wario, V. Osuna, D. Zaldivar, and M. Perez, “Fast algorithm for Multiple-Circle detection on images using Learning Automata,” arXiv Preprint arXiv:1405.5531, 2014.

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Published

2017-12-07

How to Cite

Gautam, N., & Chai, S. S. (2017). Optical Character Recognition for Brahmi Script Using Geometric Method. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-11), 131–136. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3197

Issue

Vol. 9 No. 3-11: Computing Technology Towards A Sustainable Society

Section

Articles

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