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Segmentation of Characters from Degraded Brahmi Script Images

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Applied Computer Vision and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1155))

Abstract

Segmentation of symbols or characters in the OCR process is a very critical and important phase, as it directly affects the recognition system. If objects in the image are not accurately segmented, then the recognition will also be false and this ultimately affects the performance of the system. Brahmi script contains certain alphabets which comprise some isolated symbols. In this paper, the authors have proposed an innovative approach to segment the lines from the digital Brahmi estampage document, and further segment the alphabets from the line, which are regular characters as well as special characters comprising isolated symbols. The authors have developed an algorithm that effectively segments the lines, regular characters and special characters. Advancements in the algorithm can be made to improve the results for accuracy.

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References

  1. Ojha, P.G.H.: Bharatiya Prachin Lipimala: The Palaeography of India. Munshiram Manoharlal, New Delhi (1971)

    Google Scholar 

  2. Salomon, R.: Indian Epigraphy: A Guide to the Study of Inscriptions in Sanskrit, Prakrit, and the Other Indo-Aryan Languages. Oxford University Press, New York Oxford (1998)

    Google Scholar 

  3. Bandara, D., Warnajith, N., Minato, A., Ozawa, S.: Creation of precise alphabet fonts of early Brahmi script from photographic data of ancient Sri Lankan inscriptions. Can. J. Artif. Intell. Mach. Learn. Pattern Recogn. 3(3), 33–39 (2012)

    Google Scholar 

  4. Kak, S.C.: Indus and Brahmi further connections. Cryptologia 14(2), 169–183 (1990)

    Article  MathSciNet  Google Scholar 

  5. https://en.wikipedia.org/wiki/Brahmi_script. Accessed 10 Nov 2019

  6. http://www.ancientscripts.com/brahmi.html. Accessed 10 Nov 2019

  7. Chamchong, R., Fung, C.C.: Character segmentation from ancient palm leaf manuscripts in Thailand. In: Proceedings of the 2011 Workshop on Historical Document Imaging and Processing, pp. 140–145 (2011)

    Google Scholar 

  8. Siromoney, G., Chandrasekaran, R., Chandrasekaran, M.: Machine recognition of Brahmi script. IEEE Trans. Syst. Man Cybern. SMC 13(4), 648–654 (1983)

    Google Scholar 

  9. Gajjar, T., Teraiya, R., Gohil, G., Goyani, M.: Top Down Hierarchical Histogram Based Approach for Printed Devnagri Script Character Isolation. In: Nagamalai, D., Renault, E., Dhanushkodi, M. (eds.). DPPR 2011, CCIS, vol. 205, pp. 55–64 (2011)

    Google Scholar 

  10. Saba, T., Rehman, A., Elarbi-Boudihir, M.: Methods and strategies on off-line cursive touched characters segmentation: a directional review. Artif. Intell. Rev. 42(4), 1047–1066 (2014)

    Article  Google Scholar 

  11. Alginahi, Y.M.: A survey on Arabic character segmentation. Int. J. Doc. Anal. Recogn. (IJDAR) 16(2), 105–126 (2013)

    Google Scholar 

  12. Cheng, D., Tian, F., Liu, L., Liu, X., **, Y.: Image segmentation based on multi-region multi-scale local binary fitting and Kullback-Leibler divergence. SIViP 12, 895–903 (2018)

    Article  Google Scholar 

  13. Casey, R.G., Lecolinet, E.: A survey of methods for strategies in character segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18(7), 690–706 (1996)

    Article  Google Scholar 

  14. Ramteke, A.S., Rane, M.E.: Offline handwritten Devanagari script segmentation. Int. J. Sci. Technol. Res. 1(4), 142–145 (2012)

    Google Scholar 

  15. Sinha, R.M.K., Mahabala, H.N.: Machine recognition of Devanagari script. IEEE Trans. Syst. Man Cybern. SMC 9(8), 435–441 (1979)

    Google Scholar 

  16. Pal, U., Chaudhuri, B.B.: Indian script character recognition: a survey. Pattern Recogn. 37, 1887–1899 (2004)

    Article  Google Scholar 

  17. Saba, T., Sulong, G., Rehman, A.: A survey on Methods and strategies on touched characters segmentation. Int. J. Res. Rev. Comput. Sci. (IJRRCS) 1(2), 103–114 (2010)

    Google Scholar 

  18. Kumar, M., **dal, M.K., Sharma, R.K.: Segmentation of isolated and touching characters in offline handwritten Gurmukhi script recognition. Int. J. Inf. Technol. Comput. Sci. 2, 58–63 (2014)

    Google Scholar 

  19. Shobha Rani, N., Chandan, N., Jain, S.A., Kiran, H.R.: Deformed character recognition using convolutional neural networks. Int. J. Eng. Technol. 7(3), 1599–1604 (2018)

    Article  Google Scholar 

  20. Macwan, J.J., Goswami, M.M., Vyas, A.N.: A survey on offline handwritten North Indian script symbol recognition. In: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, pp. 2747–2752 (2016)

    Google Scholar 

  21. Kathiriya, H.M., Goswami, M.M.: Word spotting techniques for Indian scripts: a survey. In: International Conference on Innovations in Power and Advanced Computing Technologies [i-PACT 2017], pp. 1–5 (2017)

    Google Scholar 

  22. Mathew, M., Singh, A.K., Jawahar, C.V.: Multilingual OCR for Indic scripts. In: 2016 12th IAPR Workshop on Document Analysis Systems (DAS), Santorini, pp. 186–191 (2016)

    Google Scholar 

  23. Kunchukuttan, A., Puduppully, R., Bhattacharyya, P.: Brahmi-Net: a transliteration and script conversion system for languages of the Indian subcontinent. In: Proceedings of NAACL-HLT, pp. 81–85 (2015)

    Google Scholar 

  24. Gautam, N., Chai, S.S.: Optical character recognition for Brahmi script using geometric method. J. Telecommun. Electron. Comput. Eng. 9, 131–136 (2017)

    Google Scholar 

  25. Warnajith, N., Bandara, D., Bandara, N., Minati, A., Ozawa, S.: Image processing approach for ancient Brahmi script analysis (Abstract). University of Kelaniya, Colombo, Sri Lanka, p. 69 (2015)

    Google Scholar 

  26. Batuwita, K.B.M.R., Bandara, G.E.M.D.C.: New segmentation algorithm for individual offline handwritten character segmentation. In: Wang, L., **, Y. (eds.) Fuzzy Systems and Knowledge Discovery. FSKD. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, vol. 3614 (2005)

    Google Scholar 

  27. Kumar, R., Singh, A.: Detection and segmentation of lines and words in Gurmukhi handwritten text. In: IEEE 2nd International Advance Computing Conference, pp. 353–356 (2010)

    Google Scholar 

  28. Anasuya Devi, H.K.: Thresholding: a pixel-level image processing methodology preprocessing technique for an OCR system for the Brahmi script. Anc. Asia 1, 161–165 (2006)

    Article  Google Scholar 

  29. Patil, C.H., Mali, S.M.: Segmentation of isolated handwritten Marathi words. In: National Conference on Digital Image and Signal Processing, pp. 21–26 (2015)

    Google Scholar 

  30. Nagane, A.S., Mali, S.M.: Segmentation of special character “∴” from degraded Brahmi script documents. In: Two Days 2nd National Conference on ‘Innovations and Developments in Computational & Applied Science’ [NCIDCAS-2018], pp. 65–67 (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. MIT-Arts, Commerce and Science College, Alandi, Pune, India

    Aniket Suresh Nagane

  2. Department of Computer Science, Savitribai Phule Pune University, Pune, India

    S. M. Mali

  3. Dr. Vishwanath Karad MIT World Peace University, Pune, India

    S. M. Mali

Authors
  1. Aniket Suresh Nagane
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  2. S. M. Mali
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Correspondence to Aniket Suresh Nagane .

Editor information

Editors and Affiliations

  1. Department of Electronics and Tele-communication Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra, India

    Brijesh Iyer

  2. Department of Computer Engineering, MGMs College of Engineering, Nanded, Maharashtra, India

    A. M. Rajurkar

  3. Department of Computer Science, East Carolina University, Greenville, NC, USA

    Venkat Gudivada

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Nagane, A.S., Mali, S.M. (2020). Segmentation of Characters from Degraded Brahmi Script Images. In: Iyer, B., Rajurkar, A., Gudivada, V. (eds) Applied Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1155. Springer, Singapore. https://doi.org/10.1007/978-981-15-4029-5_33

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  • DOI: https://doi.org/10.1007/978-981-15-4029-5_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4028-8

  • Online ISBN: 978-981-15-4029-5

  • eBook Packages: EngineeringEngineering (R0)

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