A novel nearest interest point classifier for offline Tamil handwritten character recognition

  • R. N. Ashlin DeepaEmail author
  • R. Rajeswara Rao
Theoretical advances


Handwritten character recognition is the most widely used branch of study in image pattern recognition. Tamil, the official language of Tamil Nadu in South India, Sri Lanka, Singapore and Malaysia, has a script which contains many loops and compound characters, with small differences between character classes. Most of the research on offline Tamil handwritten character recognition system was done only on few character classes as it is very difficult to distinguish between minute dissimilarities of large character classes. It is important to design a complete recognition system that can process all character classes of Tamil and distinguish natural variability between inter-class images. Unlike conventional machine learning approaches for pattern recognition problems, we have proposed a nearest interest point classifier, which can choose sufficient and necessary subset of features from a variable length high dimensional feature vector. Since this is a practical problem, in this work, a study on image to image matching is included through feature analysis without using machine learning approaches. The proposed algorithm gave a good recognition accuracy for all the character classes on the standard database available for Tamil, HP Labs offline Tamil handwritten character database. Our proposed classifier produced a recognition accuracy of 90.2% while including the whole dataset. The method has been compared with the standard classifiers and has been proved to be a state-of-the-art performance in recognition of accuracy over the previous results given in the literature.


Handwritten character recognition Pattern recognition Classification Variable length High dimensional data Speeded up robust features 



We extend our gratitude to HP Labs for providing database for our research.


  1. 1.
    Pal U, Chaudhuri BB (2004) Indian script character recognition: a survey. Pattern Recogn 37:1887–1899Google Scholar
  2. 2.
    Plamondon R, Srihari SN (2000) Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1):63–84Google Scholar
  3. 3.
    Joshi N, Sita G, Ramakrishnan AG, Madhvanath S (2004) Comparison of elastic matching algorithms for online Tamil handwritten character recognition. In: Ninth international workshop on frontiers in handwriting recognitionGoogle Scholar
  4. 4.
    Lorigo LM, Govindaraju V (2006) Offline Arabic handwriting recognition: a survey. IEEE Trans Pattern Anal Mach Intell 28(5):712–724Google Scholar
  5. 5.
    Subashini A, Kodikara ND (2011) A novel SIFT-based codebook generation for handwritten Tamil character recognition. In: 6th international conference on industrial and information systemsGoogle Scholar
  6. 6.
    Kimura F, Takashina K, Tsuruoka S, Miyake Y (1987) Modified quadratic discriminant functions and the application to Chinese character recognition. IEEE Trans Pattern Anal Mach Intell. 1:149–153Google Scholar
  7. 7.
    Liu CL, Nakashima K, Sako H, Fujisawa H (2003) Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recogn 36:2271–2285zbMATHGoogle Scholar
  8. 8.
    Kim HY, Kim JH (2001) Hierarchical random graph representation of handwritten characters and its application to hangul recognition. Pattern Recogn 34:187–201zbMATHGoogle Scholar
  9. 9.
    Gillies AM, Hepp D, Gader PD (1992) A system for recognizing handwritten words. Technical report submitted to the United States postal service, office of advanced technology, Nov. 1992Google Scholar
  10. 10.
    The Unicode Consortium (2000) The Unicode Standard 3.0. Addison Wesley publishers, HarlowGoogle Scholar
  11. 11.
    BBC (2004) India sets up classical languages. August 17, 2004.
  12. 12.
    The Hindu (2005) Sanskrit to be declared classical language. October 28, 2005. Retrieved on 2007-08-16.
  13. 13.
    Isolated Handwritten Tamil Character Dataset, hpltamil-iso-char
  14. 14.
    Raj R, Antony M, Abirami S (2016) Offline tamil handwritten character recognition using statistical based quad tree. Aust J Basic Appl Sci 10(2):103–109Google Scholar
  15. 15.
    Sundaram S, Urala KB, Ramakrishnan AG (2012) Language models for online handwritten tamil word recognition. In: Proceeding of the workshop on document analysis and recognition, December 16–16, 2012, Mumbai, IndiaGoogle Scholar
  16. 16.
    Connell SD, Jain AK (2001) Template-based online character recognition. Pattern Recogn 34:1–14zbMATHGoogle Scholar
  17. 17.
    Kunwar R, Ramakrishnan AG (2011) Tamil online handwriting recognition using fractal features. Tamil Internet 2011, At University of Pennsylvania, USAGoogle Scholar
  18. 18.
    Sundaresan CS, Keerthi SS (1999) A study of representations for pen based handwriting recognition of Tamil characters. In: Proceedings of the fifth international conference on document analysis and recognitionGoogle Scholar
  19. 19.
    Aparna KH, Subramanian V, Kasirajan M, Prakash GV, Chakravarthy VS, Madhvanath S (2004) Online handwriting recognition for Tamil. In: Ninth international workshop on frontiers in handwriting recognitionGoogle Scholar
  20. 20.
    Chinnuswamy P, Krishnamoorthy SG (1980) Recognition of hand printed tamil characters. Pattern Recogn 12:141–152Google Scholar
  21. 21.
    Suresh RM, Arumugam S, Ganesan L (1999) Fuzzy approach to recognize handwritten Tamil characters. In: Proceedings third international conference on computational intelligence and multimedia applicationsGoogle Scholar
  22. 22.
    Hewavitharana S, Fernand HC (2002) A two stage classification approach to tamil handwriting recognition. Tamil Internet, CaliforniaGoogle Scholar
  23. 23.
    Sutha J, Ramaraj N (2007) Network based offline Tamil handwritten character recognition. In: System international conference on computational intelligence and multimedia applicationsGoogle Scholar
  24. 24.
    Wahi A, Sundaramurthy S, Poovizhi P (2013) Handwritten Tamil character recognition. In: Fifth international conference on advanced computingGoogle Scholar
  25. 25.
    Kannan RJ, Prabhakar R (2008) Off-line cursive handwritten Tamil character recognition. WSEAS Trans Signal Process Arch 4(6):351–360Google Scholar
  26. 26.
    Pal U, Sharma N, Wakabayashi T, Kimura F (2008) Handwritten character recognition of popular south indian scripts. In: Doermann D, Jaeger S (eds) Arabic and chinese handwriting recognition. SACH 2006. Lecture Notes in Computer Science, vol 4768. Springer, Berlin, HeidelbergGoogle Scholar
  27. 27.
    Shanthi N, Duraiswami K (2010) A novel SVM-based handwritten Tamil character recognition system. Pattern Anal Appl 13(2):173–180MathSciNetGoogle Scholar
  28. 28.
    Vijayaraghavan P, Misha S (2015). Handwritten Tamil recognition using a convolutional neural network. NEML Poster 2015Google Scholar
  29. 29.
    Bhattacharya U, Ghosh SK, Parui SK (2007) A two stage recognition scheme for handwritten Tamil characters. In: Proceedings of the ninth international conference on document analysis and recognition (ICDAR 2007). IEEE Computer Society, Washington, DC, 511–515Google Scholar
  30. 30.
    Ashlin Deepa RN, Rao RR (2016) An efficient offline Tamil handwritten character recognition system using zernike moments and diagonal-based features. Int J Appl Eng Res 11(4):2607–2610Google Scholar
  31. 31.
    Ashlin Deepa RN, Rao RR (2017) An eigen characters method for recognition of handwritten tamil character recognition. In: Proceedings of the first international conference on intelligent computing and communication, advances in intelligent systems and computingGoogle Scholar
  32. 32.
    Ashlin Deepa RN, Rao RR (2017) A modified GA classifier for offline Tamil handwritten character recognition. Int J Appl Pattern Recognit 4(1):89–105Google Scholar
  33. 33.
    Bharath A, Madhvanath S (2012) HMM-based lexicon driven and lexicon-free word recognition for online handwritten indic scripts. IEEE Trans Pattern Anal Mach Intell 34(4):670–682Google Scholar
  34. 34.
    John J, Pramod KV, Balakrishnan K (2011) Handwritten character recognition of South Indian scripts: a review. In: National conference on Indian language computing, Kochi, Feb 19–20Google Scholar
  35. 35.
    Paulpandian T, Ganapathy V (1993) Translation and scale invariant recognition of handwritten Tamil characters using hierarchical neural networks. In: IEEE international symposium on circuits and systemsGoogle Scholar
  36. 36.
    Jain Anil K, Taxt Torfinn (1996) Feature extraction methods for character recognition-a survey. Pattern Recognit 29(4):641–662Google Scholar
  37. 37.
    Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359Google Scholar
  38. 38.
    Lia A, Jianga W, Yuana W, Daia D, Zhanga S, Weia Z (2017) An improved FAST + SURF fast matching algorithm. Proced Comput Sci 107:306–312Google Scholar
  39. 39.
    Vinay A, Vasukib V, Bhatb S, Jayanth KS, Murthya KNB, Natarajan S (2016) Two dimensionality reduction techniques for SURF based face recognition. Proced Comput Sci 85:241–248Google Scholar
  40. 40.
    Mehrotra H, Pankaj KS, Majhi B (2013) Fast segmentation and adaptive SURF descriptor for iris recognition. Math Comput Model 58:132–146Google Scholar
  41. 41.
    Li C, Khan L, Prabhakaran B, (2007). Feature selection for classification of variable length multiattribute motions. In: Multimedia data mining and knowledge discovery, pp 116–137Google Scholar
  42. 42.
    Bandyopadhyay S, Murthy CA, Pal SK (1998) Pattern classification using genetic algorithms: determination of H. Pattern Recognit Lett 19:1171–1181zbMATHGoogle Scholar
  43. 43.
    Bhatia N (2010) Survey of nearest neighbor techniques. Int J Comput Sci Inf Secur, pp 302–305Google Scholar
  44. 44.
    Duda RO, Hart PG, Stork DE (2001) Pattern classification. Wiley, New YorkzbMATHGoogle Scholar
  45. 45.
    Cover T, Hart P (1967) Nearest neighbour pattern classification. IEEE Trans Inf Theory 13:21–27zbMATHGoogle Scholar
  46. 46.
    Bailey T, Jain A (1978) A note on distance-weighted k-nearest neighbour rules. IEEE Trans Syst Man Cybern 8:311–313zbMATHGoogle Scholar
  47. 47.
    Dudani S (1976) The distance-weighted k-nearest-neighbor rule. IEEE Trans Syst Man Cybern 6:325–327Google Scholar
  48. 48.
    Beyer K, Goldstein J, Ramakrishnan R, Shaft U (1999) When is nearest neighbour meaningful? In: Proceedings of the 7th international conference of database theory ICDT 99, Lecture Notes in Computer Science, Jerusalem, Israel, January 10–12, pp 217–235Google Scholar
  49. 49.
    Houle ME, Kriegel HP, Krger P, Schubert E, Zimek A (2010) Can shared-neighbor distances defeat the curse of dimensionality? In: Proceedings of the SSDBM, pp 482–500Google Scholar
  50. 50.
    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182zbMATHGoogle Scholar
  51. 51.
    Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(2005):1226–1238Google Scholar
  52. 52.
    Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbour classification. J Mach Learn Res 10:207–244zbMATHGoogle Scholar
  53. 53.
    Xing EP, Ng AY, Jordan MI, Russell S (2002) Distance metric learning, with application to clustering with side-information. In: Advances in neural information processing systems NIPS 2001, Vancouver, Canada, December 10–12, pp 521–528Google Scholar
  54. 54.
    Goldberger J, Roweis S, Hinton G, Salakhutdinov R (2004) Neighbourhood component analysis. In: Advances in neural information processing systems NIPS, pp 513–520Google Scholar
  55. 55.
    James AP, Dimitrijev S (2012) Nearest neighbor classifier based on nearest feature decisions. Comput J 55:1072–1087Google Scholar
  56. 56.
    Zuo W, Zhang D, Wang K (2008) On kernel difference weighted k-nearest neighbor classification. Pattern Anal Appl 11:247–257MathSciNetGoogle Scholar
  57. 57.
    Shakhnarovich G, Darrell T, Indyk P (2006) Nearest-neighbor methods in learning and vision: theory and practice. MIT Press, CambridgeGoogle Scholar
  58. 58.
    Akkus A, Guvenir AH (1996) K nearest neighbor classification on feature projections. In: Proceedings of the ICML, Bari, Italy, July 3–6, pp 12–19Google Scholar
  59. 59.
    Demirz G, Guvenir AH (1997) Classification by voting feature intervals. In: Proceedings of the ECML-97, Prague, Czech Republic, April 23–25, pp 85–92. SpringerGoogle Scholar
  60. 60.
    Zhang H, Liu G, Chow TWS, Liu W (2011) Textual and visual content-based anti-phishing: a Bayesian approach. IEEE Trans Neural Netw 22(10):1532–1546Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Gokaraju Rangaraju Institute of Engineering and TechnologyBachupally, Kukatpally, HyderabadIndia
  2. 2.JNTUK University College of EngineeringVizianagaramIndia

Personalised recommendations