Development of Inter-primitive Grammar for Construction of Kannada Language Vowels and Consonants Based on Their Hierarchical Structures

  • Basavaraj S. Anami
  • Deepa S. GaragEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


Kannada, 2500 years old, the administrative and official language of Karnataka state, India. The language script comprises of 16 vowels and 34 consonants, which are formed with primitives, connection points and relative positions of other primitives. The writing skill of the language needs teaching, the teachers and parents put efforts on children to make them learn the good writing skills. Here lies the scope for automation. Robots assist children in constructing the characters of the language and improve their handwriting skills. Till date, formalism has been applied to languages to check their syntax and semantics to frame words, sentences and paragraphs. But not for the character construction, which needs a formal approach. This paper presents the development of Inter-Primitive Grammar for Construction of Kannada Language Vowels and Consonants based on their Hierarchical Structures. The unambiguous Context Free Grammar (CFG), consisting of a combination of a set of primitives written in specific sequence for construction of Kannada vowels and consonants is devised. Since two primitives are connected at a time, productions are written in Chomsky Normal Form (CNF). To corroborate the grammar, a given string of primitives as input to the tools, the corresponding transliteration code for the given character is generated. Lex and Yacc tools are used, to verify the completeness and soundness of the grammar.


Kannada Vowels Consonants Primitive Grammar Context free grammar Chomsky normal form Position label Connecting point 


  1. 1.
    Prasanna, K., Kumar, P.R.: Handwriting recognition of Kannada characters and context free grammar based syntax analysis. Int. J. Sci. Res. 1(1), 24–29 (2012)MathSciNetGoogle Scholar
  2. 2.
    Ota, I., Yamamoto, R., Nishimoto, T., Sagayama, S.: On-line handwritten Kanji string recognition based on grammar description of character structures. In: 2008 19th International Conference on Pattern Recognition ICPR (2008)Google Scholar
  3. 3.
    Wang, Y., Wang, H., Pan, C., Fang, L.: Style preserving Chinese character synthesis based on hierarchical representation of character. In: IEEE International Conference on Acoustics, Speech and Signal Processing (2008)Google Scholar
  4. 4.
    Ota, I., Yamamoto, R., Sako, S., Sagayama, S.: Online handwritten Kanji recognition based on inter-stroke grammar. In: 9th International Proceedings on Document Analysis and Recognition, pp. 1188–1192. IEEE Computer Society, Washington, DC (2007)Google Scholar
  5. 5.
    Kim, H.J., Kim, S.K.: On-line recognition of cursive Korean characters using art-based stroke classification (recognition of cursive Korean characters). Int. J. Pattern Recogn. Artif. Intell. 10(7), 791–812 (1996)CrossRefGoogle Scholar
  6. 6.
    Kim, S.K., Kim, J.W., Kim, H.J.: On-line recognition of cursive Korean characters using neural networks. Neurocomputing 10(3), 291–305 (1996)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Kim, P.K., Lee, J.K., Kim, H.J.: Handwritten Korean character recognition by stroke extraction and representation. In: Proceedings of TENCON 1993 in Computer, Communication, Control and Power Engineering (1993)Google Scholar
  8. 8.
    Wang, J.H., Ozawa, S.: Automated generation of Chinese character structure data based on extracting the strokes. In: Proceedings of the 2nd International Conference on Document Analysis and Recognition (1993)Google Scholar
  9. 9.
    Ohmori, K.: On-line handwriting Kanji character recognition using hypothesis generation in the space of hierarchical knowledge. In: 3rd International Workshop on Frontiers in Handwriting Recognition, pp. 242–251 (1993)Google Scholar
  10. 10.
    Nagahashi, H., Nakatsuyama, M.: A pattern description and generation method of structural characters. IEEE Trans. Pattern Anal. Mach. Intell. 8(1), 112–118 (1986)CrossRefGoogle Scholar
  11. 11.
    Indira, K., Selvi, S.S.: Kannada character recognition system: a review. Inter JRI Sci. Technol. 1(2) (2009)Google Scholar
  12. 12.
    Pal, U., Chaudhuri, B.B.: Indian script character recognition: a survey. Pattern Recogn. 37(9), 1887–1899 (2004)CrossRefGoogle Scholar
  13. 13.
    Johnson, S.C.: Yacc: yet another compiler-compiler. Computing Science Technical report no. 32. Bell Laboratories, Murray Hill (1975)Google Scholar
  14. 14.
    Lesk, M.E., Schmidt, E.: Lex - a lexical analyzer generator. Computing Science Technical report no. 39. Bell Laboratories, Murray Hill (1975)Google Scholar
  15. 15.
    Levine, J.R., Mason, T., Brown, D.: Lex & Yacc. O’Reilly & Associates Inc., Sebastopol (1992)Google Scholar
  16. 16.
    Kamble, P.M., Hegadi, R.S.: Handwritten Marathi character recognition using R-HOG feature. In: International Conference on Advanced Computing Technologies and Applications (2015)Google Scholar
  17. 17.
    Santosh, K.C., Nattee, C.: Stroke number and order free handwriting recognition for Nepali. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 990–994. Springer, Heidelberg (2006). Scholar
  18. 18.
    Santosh, K.C., Nattee, C.: A comprehensive survey on on-line handwriting recognition technology and its real application to the Nepalese natural handwriting. Kathmandu Univ. J. Sci. Eng. Technol. 5(1), 31–55 (2009)Google Scholar
  19. 19.
    Kamble, P.M., Hegadi, R.S.: Geometrical features extraction and KNN based classification of handwritten Marathi characters. In: World Congress on Computing and Communication Technologies (2017)Google Scholar
  20. 20.
    Santosh, K.C., Nattee, C.: Spatial similarity based stroke number and order free clustering. In: International Conference on Frontiers in Handwriting Recognition, Kolkata, India (2016)Google Scholar
  21. 21.
    Santosh, K.C., Nattee, C.: Relative positioning of stroke based clustering: a new approach to on-line handwritten Devanagari character recognition. Int. J. Image Graph. 12(02), 1250016 (2012)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.K.L.E. Institute of TechnologyHubballiIndia

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