Skip to main content

Handwritten English Alphabets Recognition System

  • Conference paper
  • First Online:
Advanced Computing (IACC 2023)

Abstract

The objective of this study is to create a Handwritten English Alphabet Recognition System, emphasizing signature recognition. In a global context where handwritten records and signatures play pivotal roles in various sectors, including legal, finance, and authentication, the demand for accurate and efficient recognition methods is paramount. This research project endeavors to construct a resilient system that can precisely identify and categorize handwritten English letters and signatures through the application of machine learning techniques, notably deep learning. The system employs convolutional and recurrent neural networks to adapt to diverse writing styles and varying levels of complexity.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ding, K., Liu, Z., Jin, L., Zhu, X.: A comparative study of GABOR feature and gradient feature for handwritten Chinese character recognition. In: International Conference on Wavelet Analysis and Pattern Recognition, p. 11821186, Beijing, China, 2–4 November 2007

    Google Scholar 

  2. Charles, P.K., Harish, V., Swathi, M., Deepthi, C.H.: A review on the various techniques used for optical character recognition. Int. J. Eng. Res. Appl. 2(1), 659–662 (2012)

    Google Scholar 

  3. Bahlmann, C., Haasdonk, B., Burkhardt, H.: Online handwriting recognition with support vector machines-a kernel approach. In: IEEE Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition, pp. 49–54 (2002)

    Google Scholar 

  4. Neetu, B.: Optical character recognition techniques. Int. J.Adv. Res. Comput. Sci. Softw. Eng. 4(5) (2014)

    Google Scholar 

  5. Pradeep, J., Srinivasan, E., Himavathi, S.: Diagonal based feature extraction for handwritten character recognition system using neural network. In: 3rd IEEE International Conference on Electronics Computer Technology, vol. 4, pp. 364–368 (2011)

    Google Scholar 

  6. Navneet, D., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the CVPR2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1. San Diego, CA, USA, 20–25 June 2005

    Google Scholar 

  7. Simonyan, K., Andrew, Z.: Very deep convolutional networks for large-scale image recognition. arXiv (2004)

    Google Scholar 

  8. Bajaj, R., Dey, L., Chaudhury, S.: Devnagari numeral recognition by combining decision of multiple connectionist classifiers. Sadhana, part. 1, 27, 59–72 (2002)

    Google Scholar 

  9. Lorigo, L.M., Govindaraju, V.: Offline Arabic handwriting recognition: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 28(5) (2006)

    Google Scholar 

  10. Kumar, G., Bhatia, P.K., Banger, I.: Analytical review of preprocessing techniques for offline handwritten character recognition. In: 2nd International Conference on Emerging Trends in Engineering & Management, ICETEM (2013)

    Google Scholar 

  11. Espana-Boquera, S., Castro-Bleda, M.J., Gorbe-Moya, J., Zamora-Martinez, F.: Improving offline handwritten text recognition with hybrid HMM/ANN models. IEEE Trans. P

    Google Scholar 

  12. Brakensiek, A., Rottland, J., Kosmala, A., Rigoll, G.: Offline handwriting recognition using various hybrid modeling techniques & character N-Grams. http://irs.ub.rug.nl/dbi/4357a84695495

  13. Kumar, G., Kumar, S.: CNN based handwritten Devanagari digits recognition. Int. J. Comput. Sci. Eng. 5, 71–74 (2017)

    Google Scholar 

  14. Arora, S.: Combining multiple feature extraction techniques for handwritten Devanagari character recognition. In: IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA (2008)

    Google Scholar 

  15. Singh, D., Khan, M. A., Bansal, A., Bansal, N.: An application of SVM in character recognition with chain code. In: Communication, Control and Intelligent Systems (CCIS), pp. 167–171 (2015)

    Google Scholar 

  16. Som, T., Saha, S.: Handwritten character recognition using fuzzy membership function. Int. J. Emerg. Technol. Sci. Eng. 5(2), 11–15 (2011)

    Google Scholar 

  17. Hanmandlu, M., Murthy, O.R.: Fuzzy model based recognition of handwritten numerals. Pattern Recog. 40, 1840–1854 (2007)

    Google Scholar 

  18. Patnaik, S.S., Panda, A.K.: Particle swarm optimization and bacterial foraging optimization techniques for optimal current harmonic mitigation by employing active power filter applied computational intelligence and soft computing. 2012, 897127 (2012)

    Google Scholar 

  19. Jawad, H., Olivier, P., Jinchang, R., Jianmin, J.: Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition. Knowl.-Based Syst. 24, 680–688 (2011)

    Article  Google Scholar 

  20. Plamondon, R., Srihari, S.: Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22, 63–68 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Raunak Kumar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kumar, R., Patra, S., Singh, A.P. (2024). Handwritten English Alphabets Recognition System. In: Garg, D., Rodrigues, J.J.P.C., Gupta, S.K., Cheng, X., Sarao, P., Patel, G.S. (eds) Advanced Computing. IACC 2023. Communications in Computer and Information Science, vol 2053. Springer, Cham. https://doi.org/10.1007/978-3-031-56700-1_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-56700-1_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56699-8

  • Online ISBN: 978-3-031-56700-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics