Skip to main content
SpringerLink
Log in

Efficient Feature Extraction Techniques for Offline Handwritten Gurmukhi Character Recognition

  • Short Communication
  • Published:
National Academy Science Letters Aims and scope Submit manuscript

Abstract

As a result of advances in optical character recognition research, several techniques for handwritten character recognition have surfaced. These techniques require good quality features as their input for the recognition process. In this paper, we have proposed two different feature extraction techniques, namely, parabola curve fitting based features and power curve fitting based features for offline handwritten Gurmukhi character recognition. In order to assess the quality of features in offline handwritten Gurmukhi character recognition, we have also analyzed the performance of other recently proposed feature extraction techniques, namely, zoning, diagonal, directional, transition, intersection and open end points, gradient and chain code features. Each technique has been tested by using 3,500 images of offline handwritten Gurmukhi characters. Support vector machine (SVM) and k-NN classifiers have been used to recognize the characters in this work. The proposed system achieves a recognition accuracy of 98.10 and 97.14 % using k-NN and SVM classifiers, respectively, when power curve fitting based features are used as input to the classification process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Lorigo LM, Govindaraju V (2006) Offline arabic handwriting recognition: a survey. IEEE Trans PAMI 28(5):712–724

    Article  Google Scholar 

  2. Plamondon R, Srihari SN (2000) On-line and off- line handwritten character recognition: a comprehensive survey. IEEE Trans PAMI 22(1):63–84

    Article  Google Scholar 

  3. Zhang TY, Suen CY (1984) A fast parallel algorithm for thinning digital patterns. Commun ACM 27(3):236–239

    Article  Google Scholar 

  4. Hanmandlu M, Grover J, Madasu VK, Vasikarla S (2007) Input fuzzy for the recognition of handwritten Hindi numeral. In: Proceedings of the ITNG, pp 208–213

  5. Kumar M, Sharma RK, Jindal MK (2011) SVM based offline handwritten Gurmukhi character recognition. In: Proceedings of the SCAKD, Vol 758, pp 51–62

  6. Sharma DV, Jhajj P (2010) Recognition of isolated handwritten characters in Gurmukhi script. Int J Comput Appl 4(8):9–17

    Google Scholar 

  7. Sethi K, Chatterjee B (1976) Machine recognition of constrained hand-printed Devanagari numerals. J Inst Electr Telecom Eng 22:532–535

    Google Scholar 

  8. Bansal V, Sinha RMK (2000) Integrating knowledge sources in Devanagari text recognition. IEEE Trans Syst, Man Cybern 30(4):500–505

    Article  Google Scholar 

  9. Sharma N, Pal U, Kimura F, Pal S (2006) Recognition of off-line handwritten Devanagari characters using quadratic classifier. In: Proceedings of the ICVGIP, pp 805–816

  10. Pardeep J, Srinivasan E, Himavathi S (2010) Diagonal feature extraction based handwritten character system using neural network. Int J Comput Appl 8(9):17–22

    Google Scholar 

  11. Rajput GG, Mali SM (2010) Marathi handwritten numeral recognition using Fourier descriptors and normalized chain code. In: Proceedings of the recent trends in image processing and pattern recognition (RTIPPR), pp 141–145

  12. Rodriguez JA, Perronnin F (2008) Local gradient histogram features for word spotting in unconstrained handwritten documents. In: Proceedings of the international conference on frontiers in handwriting recognition, pp 7–12

  13. Basu S, Das N, Sarkar R, Kundu M, Nasipuri M, Basu DK (2009) A hierarchical approach to recognition of handwritten Bangla characters. Pattern Recognit 42(7):1467–1484

    Article  Google Scholar 

  14. Garain U, Chaudhuri BB, Pal TT (2002) Online handwritten Indian script recognition: a human motor function based framework, In: Proceedings of the 16th international conference on pattern recognition (ICPR), Vol 3, pp 164–167

  15. Roy K, Vajda S, Pal U, Chaudhuri BB (2004) A system towards Indian postal automation, In: Proceedings of the 9th international workshop on frontiers in handwriting recognition (IWFHR), pp 361–367

  16. Sharma A, Kumar R, Sharma RK (2008) Online handwritten Gurmukhi character recognition using elastic matching. Int J Congr Image Signal Process 2:391–396

    Article  Google Scholar 

  17. Kumar M, Jindal MK, Sharma RK (2011) Review on OCR for handwritten indian scripts character recognition. In: Proceeding of international conference on DPPR 2011, pp 268–276

  18. Ashok J, Rajan EG (2011) Offline handwritten character recognition using radial basis function. Int J Adv Netw Appl 2(4):792–795

    Google Scholar 

  19. Pal U, Wakabayashi T, Kimura F (2007) Handwritten Bangla compound character recognition using gradient feature. In: Proceedings of the 10th international conference on information technology (ICIT), pp 208–213

  20. Roy K, Alaei A, Pal U (2010) Word-wise handwritten Persian and Roman script identification. In: Proceedings of the international conference on frontiers in handwriting recognition (ICFHR), pp 628–633

  21. Kumar D (2008) AI approach to hand written Devanagri script recognition. In: Proceedings of the IEEE region 10th international conference on EC3-energy,computer,communication and control systems, Vol 2, pp 229–237

  22. Pal U, Wakabayashi T, Kimura F (2007) A system for off-line Oriya handwritten character recognition using curvature feature. In: Proceedings of 10th International Conference on Information Technology (ICIT), pp 227–229

  23. Pal U, Wakabayashi T, Kimura F (2009) Comparative study of Devanagari handwritten character recognition using different feature and classifiers. In: Proceedings of 10th international conference document analysis and recognition (ICDAR ‘09), pp 1111–1115

  24. Bhattacharya U, Shridhar M, Parui SK (2006) On recognition of handwritten Bangla characters. In: Proceedings of International Conference on Computer Vision, Graphics and Image Processing (ICVGIP), pp 817–828

  25. Lehal GS, Singh C (2000) A Gurmukhi script recognition system. In: Proceedings of the 15th ICPR, pp 557–560

  26. Jindal MK, Lehal GS, Sharma RK (2009) On segmentation of touching characters and overlapping lines in degraded printed Gurmukhi script. Int J Image Graph 9(3):321–353

    Article  Google Scholar 

  27. http://en.wikipedia.org/wiki/Punjabi_language

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Panjab University Rural Centre, Kauni, Muktsar, Punjab, India

    Munish Kumar

  2. School of Mathematics & Computer Applications, Thapar University, Patiala, Punjab, India

    R. K. Sharma

  3. Department of Computer Science & Applications, Panjab University Regional Centre, Muktsar, Punjab, India

    M. K. Jindal

Authors
  1. Munish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. K. Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. K. Jindal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Munish Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, M., Sharma, R.K. & Jindal, M.K. Efficient Feature Extraction Techniques for Offline Handwritten Gurmukhi Character Recognition. Natl. Acad. Sci. Lett. 37, 381–391 (2014). https://doi.org/10.1007/s40009-014-0253-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40009-014-0253-4

Keywords

Search

Navigation