Skip to main content
SpringerLink
Log in
Book cover

Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals pp 121–133Cite as

Offline Handwritten Gurumukhi Character Recognition System Using Deep Learning

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1064))

Abstract

Currently, Gurumukhi—a religion-specific language originating from India, ranks as the 14th most spoken language and the 18th most popular writing script language of the entire world. However, while there exists a large body of literature related to recognition of handwritten texts in various languages, number of publications related to recognition of Indian handwritten scripts is considerably smaller. It concerns also the case of Gurumukhi. Hence, in the current contribution, we consider Gurumukhi handwritten character recognition, to fill the existing practical gap. The proposed approach is based on deep convolutional neural networks and has been applied to the 35 core Gurumukhi characters. Obtained results are promising, as accuracy of 98.32% has been achieved for the training dataset, and 74.66%, on the test data. These results are comparable to results reported in earlier research but have been obtained without any feature extraction or post-processing.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. R.D. Zarro, M.A. Anwer, Recognition-based online Kurdish character recognition using hidden Markov model and harmony search. Eng. Sci. Technol. Int. J. 20(2), 783–794 (2017)

    Article  Google Scholar 

  2. Z. Alom, P. Sidike, M. Hasan, T.M. Taha, V.K. Asari, Handwritten Bangla character recognition using the state-of-art deep convolutional neural networks. Comput. Vis. Pattern Recognit., 1–12 (2017)

    Article  Google Scholar 

  3. D. Sharma, U. Jain, Recognition of isolated handwritten characters of Gurumukhi script using neocognitron. Int. J. Comput. Appl. 10(8), 975–8887 (2010)

    Google Scholar 

  4. G. Lehal, C. Singh, A Gurumukhi script recognition system. Int. Conf. Pattern Recognit. 2(2), 557–560 (2000)

    Article  Google Scholar 

  5. N. Kumar, S. Gupta, A novel handwritten Gurumukhi character recognition system based on deep neural networks. Int. J. Pure Appl. Math. 117(21), 663–678 (2017)

    Google Scholar 

  6. M. Kumar, M.K. Jindal, R.K. Sharma, k-nearest neighbor based offline handwritten Gurumukhi character recognition, in Proceedings of the 2011 International Conference on Image Information Processing (ICIIP) (2011)

    Google Scholar 

  7. M. Kumar, M.K. Jindal, R.K. Sharma, Classification of characters and grading writers in offline handwritten Gurumukhi script, in 2011 International Conference on Image Information Processing, Shimla (2011), pp. 1–4

    Google Scholar 

  8. N. Kumar, S. Gupta, Offline handwritten Gurumukhi character recognition: a review. Int. J. Softw. Eng. Its Appl. 10(5), 77–86 (2016)

    Google Scholar 

  9. S. Acharya, A.K. Pant, P. K. Gyawali, Deep learning based large scale handwritten Devanagari character recognition, in 2015 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Kathmandu (2015), pp. 1–6. https://doi.org/10.1109/skima.2015.7400041

  10. S. Roy, N. Das, M. Kundu, M. Nasipuri, Handwritten isolated Bangla compound character recognition: a new benchmark using a novel deep learning approach. Pattern Recognit. Lett. 90, 15–21 (2017)

    Article  Google Scholar 

  11. H. Kaur, S. Rani, Handwritten Gurumukhi character recognition using convolution neural network. Int. J. Comput. Intell. Res. 13.5 90, 933–943 (2017)

    Google Scholar 

  12. S. Naz, A.I. Umar, R. Ahmad, I. Siddiqi, S.B. Ahmed, M.I. Razzak, F. Shafait, Urdu Nastaliq recognition using convolutional–recursive deep learning. Neurocomputing 243, 80–87 (2017)

    Article  Google Scholar 

  13. B. Chakraborty, B. Shaw, J. Aich, U. Bhattacharya, S.K. Parui, Does deeper network lead to better accuracy: a case study on handwritten Devanagari characters, in 2018 13th IAPR International Workshop on Document Analysis Systems (2018), pp. 411–416

    Google Scholar 

  14. M.M. Rahman, M.A.H. Akhand, S. Islam, P. Chandra Shill, M.M. Hafizur Rahman, Bangla handwritten character recognition using convolutional neural network. Int. J. Image, Graph. Signal Process. 7(8), 42–49 (2015)

    Article  Google Scholar 

  15. M. Mhiri, C. Desrosiers, M. Cheriet, Convolutional pyramid of bidirectional character sequences for the recognition of handwritten words. Pattern Recognit. Lett. 111, 87–93 (2018)

    Article  Google Scholar 

  16. M.Z. Alom, P. Sidike, T.M. Taha, V.K. Asari, Handwritten Bangla Digit Recognition Using Deep Learning (2017). arXiv:1705.02680

Download references

Author information

Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Udit Jindal & Sheifali Gupta

  2. Bharati Vidyapeeth’s Institute of Computer Applications and Management, New Delhi, India

    Vishal Jain

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

Authors
  1. Udit Jindal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sheifali Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vishal Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marcin Paprzycki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcin Paprzycki .

Editor information

Editors and Affiliations

  1. Faculty of Engineering and IT, University of Technology Sydney, Broadway, NSW, Australia

    Lakhmi C. Jain

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  3. Department of Computer Science, University of Milan, Milan, Italy

    Vincenzo Piuri

  4. Department of Automatics and Applied Software, University of Arad, Arad, Romania

    Valentina E. Balas

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jindal, U., Gupta, S., Jain, V., Paprzycki, M. (2020). Offline Handwritten Gurumukhi Character Recognition System Using Deep Learning. In: Jain, L., Virvou, M., Piuri, V., Balas, V. (eds) Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals. Advances in Intelligent Systems and Computing, vol 1064. Springer, Singapore. https://doi.org/10.1007/978-981-15-0339-9_11

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-0339-9_11

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0338-2

  • Online ISBN: 978-981-15-0339-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation