Skip to main content
Springer Nature Link
Log in

EffKannadaRes-NeXt: An efficient residual network for Kannada numeral recognition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this article a framework regarding correct and efficient identification of Kannada handwritten numerals has been proposed. The EffKannadaRes-NeXt model is based on deep residual network ResNeXt and takes binary as well as gray-scale representations of numeral images into consideration. The study deals with handling numeral images from MNIST-sized Kannada-MNIST dataset and Dig-MNIST dataset, an out-of-domain test dataset. The test datasets derive test sets from two different scenarios and these sets are beneficial for evaluating the robustness of the model. The EffKannadaRes-Next is observed to achieve an accuracy of 97.81% and 97.37% on the Kannada-MNIST test dataset along with 82.08% and 81.67% on Dig-MNIST dataset. A comparison of results with those available in the literature is performed and a close agreement shows the versatility of the present technique.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Ahlawat S, Choudhary A, Nayyar A, Singh S, Yoon B (2020) Improved handwritten digit recognition using convolutional neural networks (cnn). Sensors 20(12):3344

    Article  Google Scholar 

  2. Ahlawat S, Rishi R (2017) Off-line handwritten numeral recognition using hybrid feature set–a comparative analysis. Procedia Comput Sci 122:1092–1099

    Article  Google Scholar 

  3. Al-wajih E, Ghazali R, Hassim YMM (2020) Residual neural network vs local binary convolutional neural networks for bilingual handwritten digit recognition. In: International conference on soft computing and data mining. Springer, pp 25–34

  4. Asha K, Krishnappa H (2018) Kannada handwritten document recognition using convolutional neural network. In: 2018 3Rd international conference on computational systems and information technology for sustainable solutions (CSITSS). IEEE, pp 299–301

  5. Basri R, Haque MR, Akter M, Uddin MS (2020) Bangla handwritten digit recognition using deep convolutional neural network. In: Proceedings of the International Conference on Computing Advancements, pp 1–7

  6. Bora R, Bisht A, Saini A, Gupta T, Mittal A (2017) Isl gesture recognition using multiple feature fusion. In: 2017 International conference on wireless communications, signal processing and networking (wiSPNET). IEEE, pp 196–199

  7. Chaithra D, Indira K (2017) Handwritten online character recognition for single stroke kannada characters. In: 2017 2nd IEEE international conference on recent trends in electronics, information & communication technology (RTEICT). IEEE, pp 548–552

  8. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  9. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inform Theor 13(1):21–27

    Article  Google Scholar 

  10. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection

  11. Ghadekar P, Ingole S, Sonone D (2018) Handwritten digit and letter recognition using hybrid dwt-dct with knn and svm classifier. In: 2018 Fourth international conference on computing communication control and automation (ICCUBEA). IEEE, pp 1–6

  12. Ghosh M, Guha R, Mondal R, Singh PK, Sarkar R, Nasipuri M (2018) Feature selection using histogram-based multi-objective ga for handwritten devanagari numeral recognition. In: Intelligent engineering informatics. Springer, pp 471–479

  13. Ghosh T, Abedin MMHZ, Chowdhury SM, Tasnim Z, Karim T, Reza SS, Saika S, Yousuf MA (2020) Bangla handwritten character recognition using mobilenet v1 architecture. Bull Electric Eng Inform 9(6):2547–2554

    Article  Google Scholar 

  14. Guha R, Das N, Kundu M, Nasipuri M, Santosh K (2020) Devnet: An efficient cnn architecture for handwritten devanagari character recognition. In: International Journal of Pattern Recognition and Artificial Intelligence, p 2052009

  15. Gupta D, Bag S (2020) Cnn-based multilingual handwritten numeral recognition: A fusion-free approach. Expert Syst Appl 113784

  16. Hallur VC, Hegadi RS, Hegadi RS (2019) Handwritten kannada numerals recognition by using zone features and cnn classifier. Int J Technol Human Interact (IJTHI) 15(4):63–79

    Article  Google Scholar 

  17. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  18. Jiang W, Zhang L (2020) Edge-siamnet and edge-triplenet: New deep learning models for handwritten numeral recognition. IEICE Trans Inf Syst 103 (3):720–723

    Article  Google Scholar 

  19. Karthik S, Murthy KS (2019) Deep belief network based approach to recognize handwritten kannada characters using distributed average of gradients. Clust Comput 22(2):4673–4681

    Article  Google Scholar 

  20. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  21. Kumar M, Jindal M, Sharma R, Jindal SR (2018) Offline handwritten numeral recognition using combination of different feature extraction techniques. Nat Academy Sci Lett 41(1):29–33

    Article  Google Scholar 

  22. Kumar M, Jindal MK, Sharma RK, Jindal SR (2019) Character and numeral recognition for non-indic and indic scripts: A survey. Artif Intell Rev 52(4):2235–2261

    Article  Google Scholar 

  23. Maaten Lvd, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(Nov):2579–2605

    MATH  Google Scholar 

  24. Mhapsekar M, Mhapsekar P, Mhatre A, Sawant V (2020) Implementation of residual network (resnet) for devanagari handwritten character recognition. In: Advanced computing technologies and applications. Springer, pp 137–148

  25. Mukarambi G, Dhandra B (2020) Energy-based features for kannada handwritten digit recognition. Int J Computat Vision Robot 10(2):156–166

    Article  Google Scholar 

  26. Nan F, Zeng Q, Xing Y, Qian Y (2020) Single image super-resolution reconstruction based on the resnext network. Multimed Tools Appl 1–12

  27. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  28. Pasha S, Padma M (2015) Handwritten kannada character recognition using wavelet transform and structural features. In: 2015 International conference on emerging research in electronics, computer science and technology (ICERECT). IEEE, pp 346–351

  29. Prabhu VU (2019) Kannada-mnist: A new handwritten digits dataset for the kannada language. arXiv:1908.01242

  30. Pramanik R, Dansena P, Bag S (2018) A study on the effect of cnn-based transfer learning on handwritten indic and mixed numeral recognition. In: Workshop on document analysis and recognition. Springer, pp 41–51

  31. Prasad MM (2019) Improvement in the online handwritten kannada numeral recognition with the difference feature

  32. Roy A (2020) Akhcrnet: Bengali handwritten character recognition using deep learning. Tech. rep. EasyChair

  33. Saini A (2016) An optimized interconnection network based on genetic algorithm. In: 2016 Fourth international conference on parallel, distributed and grid computing (PDGC). IEEE, pp 468–473

  34. Saini A, Gupta T, Kumar R, Gupta AK, Panwar M, Mittal A (2017) Image based indian monument recognition using convoluted neural networks. In: 2017 International conference on big data, iot and data science (BID). IEEE, pp 138–142

  35. Saini A et al (2015) Designing an interconnection network using genetic algorithm

  36. Saini S, Tripathi V, Verma A (2020) Employing night-time light images for wealth assessment in india: A machine learning perspective. In: Proceedings of ICETIT 2019. Springer, pp 613–622

  37. Sharma N, Pal U, Kimura F (2006) Recognition of handwritten kannada numerals. In: 9Th international conference on information technology (ICIT’06). IEEE, pp 133–136

  38. Shukla P, Gupta T, Saini A, Singh P, Balasubramanian R (2017) A deep learning frame-work for recognizing developmental disorders. In: 2017 IEEE Winter conference on applications of computer vision (WACV). IEEE, pp 705–714

  39. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  40. Trivedi A, Srivastava S, Mishra A, Shukla A, Tiwari R (2018) Hybrid evolutionary approach for devanagari handwritten numeral recognition using convolutional neural network. Procedia Computer Science 125:525–532

    Article  Google Scholar 

  41. Upadhye GD, Kulkarni U (2019) Pattern classification of handwritten kannada digits using customized cnn. In: International conference on soft computing and signal processing. Springer, pp 613–620

  42. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometrics Intell Labor Syst 2(1-3):37–52

    Article  Google Scholar 

  43. Xie S, Girshick R, Dollár P., Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492–1500

  44. Zhan H, Chowdhury PN, Pal U, Lu Y (2019) Handwritten digit string recognition for indian scripts. In: Asian conference on pattern recognition. Springer, pp 262–273

  45. Zhang Z, Sabuncu M (2018) Generalized cross entropy loss for training deep neural networks with noisy labels. In: Advances in neural information processing systems, pp 8778–8788

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Aradhya Saini

  2. VIT Chennai, Chennai, India

    Sandeep Daniel

  3. Jaypee Institute of Information Technology, Noida, India

    Satyam Saini

  4. Raman Classes, Roorkee, India

    Ankush Mittal

Authors
  1. Aradhya Saini
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Sandeep Daniel
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Satyam Saini
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ankush Mittal
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Aradhya Saini.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saini, A., Daniel, S., Saini, S. et al. EffKannadaRes-NeXt: An efficient residual network for Kannada numeral recognition. Multimed Tools Appl 80, 28391–28417 (2021). https://doi.org/10.1007/s11042-021-10797-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-10797-0

Keywords