International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

A Survey on Intelligent Kannada Inscription Character Recognition Using OCR and Machine Learning

Abstract

Reading a Kannada inscription character in manual is a time-consuming task. Character recognition system using manual takes over a month to identify the character. Over the decades, the character has evolved into different shapes. The archaeology specialists examine each of this character individually to identify a character. Manual techniques are inconsistent. Reading the Kannada stone inscriptions character directly would be time-consuming and inefficient task. For both the public and archaeologists, automating the character identification procedure will be advantageous. This is the primary objective of this research, which focused on developing a method to identify ancient Kannada inscription characters using optical character recognition method. The time period from 8 AD to 12 AD was chosen to limit the research scope of the study. The final outcome of the research study has two elements. When a scanned image of the inscription is entered by the user, the OCR module makes it easy way to identify the characters and converted into modern character. The GIS module is used to provide a map for features that track inscription sites and make it easier for users to view the locations of inscriptions. The OCR solution with the best results was further applied after each one was evaluated.

Country : India

1 Shivakumar B2 Dr. Asha K R3 Dr. Kavyashree N

  1. Research Scholar, SSAHE, Tumkur, India
  2. Associate Professor, Dept. of CSE, SSIT, Tumkur, India
  3. Assistant Professor, Dept. of CSE, SSIT, Tumkur, India

IRJIET, Volume 8, Issue 7, July 2024 pp. 154-158

doi.org/10.47001/IRJIET/2024.807016

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References

  1. S. Bhat and B. Achar, H.V, "Character recognition and Period prediction of ancient Kannada Epigraphical scripts," International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 3, no. 1, pp. 114-118, 2016.
  2. Dr.H.S.Mohana, “Era identification and recognition of Ganga and Hoysala phase Kannada stone inscriptions using advance recognition algorithm”, 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT).
  3. S. Rajakumar, V. Subbaih Bharati, “Eighth century Tamil consonants recognition from stone inscriptions”, IEEE, 2002.
  4. G. Liu and F. Gao, "Oracle-Bone Inscription Recognition Based on Deep Convolutional Neural Network," Journal of Computers, vol. 13, no. 12, pp. 1442-1450, 2018.
  5. ChakiN, Shaikh, SH & Saeed, K 2014, “A comprehensive survey on image binarization techniques”, In: Chaki N (ed) Exploring image binarization techniques. Springer, New Delhi, pp. 5-15.
  6. Das, S & Banerjee, S 2015, “An algorithm for Japanese character recognition”. International Journal of Image, Graphics and Signal Processing (IJIGSP), vol. 7, no. 1, pp. 9-15.
  7. Jayanthi, N, Tomar, A, Raj, A, Indu, S & Chaudhury, S 2014, “Digitization of historic inscription images using cumulants based simultaneous blind source extraction”. Proceedings of the Indian Conference on Computer Vision Graphics and Image Processing, ACM, p. 51.
  8. Chandrakala, HT & Thippeswamy, G 2017, “Epigraphic Document Image Enhancement Using Retinex Method”. In International Symposium on Signal Processing and Intelligent Recognition Systems (pp. 178-184). Springer, Cham.
  9. Soumya, A, & Kumar, GH, 2014, “Pre-processing of camera captured inscriptions and segmentation of handwritten Kannada text”. Int J Adv Res Comput Communication Engineering vol. 3 no.5 pp. 6794-6803.
  10. Lu, S, Su, B & Tan Chew Lim 2010, “Document image binarization using background estimation and stroke edges”. Int J Doc Anal Recognition 13.4, pp. 303-314.
  11. Pannirselvam, S & Ponmani, S, 2014, “A Novel Hybrid Model for Tamil Handwritten Character Segmentation”. International Journal of Scientific & Engineering Research, vol. 5 no. 11, pp. 271-275.
  12. Sridevi, N & Subashini, P 2012, “Segmentation of Text Lines and Characters in Ancient Tamil Script Documents using Computational Intelligence Techniques”. International Journal of Computer Applications, vol. 52, no. 14.
  13. Aouadi, N & Kacem, A 2017, “A proposal for touching component segmentation in Arabic manuscripts”. Pattern Analysis and Applications, vol. 20, no. 4, pp. 1005-1027.
  14. Dave, N 2015, “Segmentation methods for hand written character recognition”. International journal of signal processing, image processing and pattern recognition, vol. 8, no. 4, pp. 155-164.
  15. Roy, PP, Bhunia, AK, Das, A, Dey, P & Pal, U, 2016, “HMM-based Indic handwritten word recognition using zone segmentation”. Pattern Recognition, vol. 60, pp. 1057-1075. 75.
  16. Ryu, J, Koo, HI & Cho, NI, 2015, “Word segmentation method for handwritten documents based on structured learning”. IEEE Signal Processing Letters, vol. 22 no. 8, pp. 1161-1165.

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