Skip to main content
SpringerLink
Log in

Applying Context to Handwritten Character Recognition

  • Conference paper
  • First Online:
Artificial Intelligence Methods in Intelligent Algorithms (CSOC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 985))

Included in the following conference series:

Abstract

Attempts to automate handwritten character recognition date back to the 1960s, but progress over the past two decades shows extremely accurate recognition of printed characters in English. The most common approaches used today apply a form of machine learning such as support vector machines (SVM) or neural networks. While highly accurate, these forms of machine learning do not attempt to apply higher-level knowledge to improve performance. This paper presents research applying SVM-trained recognizers supplemented with domain knowledge to provide top-down guidance in an attempt to improve recognition accuracy.

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

Access this chapter

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

Institutional subscriptions

References

  1. Herbert, H.: The history of OCR, optical character recognition. Recognition Technologies Users Association, Manchester Center, VT (1982)

    Google Scholar 

  2. Kumar, G., Bhatia, P.K.: A detailed review of feature extraction in image processing systems. In: IEEE 2014 Fourth International Conference on Advanced Computing & Communication Technologies, India, pp. 5–12. IEEE (2014)

    Google Scholar 

  3. Saba, T., Almazyad, A., Rehman, A.: Language independent rule based classification of printed & handwritten text. In: 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1–4. IEEE (2015)

    Google Scholar 

  4. De Stefano, C., Fontanella, F., Marrocco, C., Di Freca, A.S.: A GA-based feature selection approach with an application to handwritten character recognition. Pattern Recogn. Lett. 35, 130–141 (2014)

    Article  Google Scholar 

  5. Sabeenian, R., Paramasivam, M., Dinesh, P., Adarsh, R., Kumar, G.: Classification of handwritten Tamil characters in palm leaf manuscripts using SVM based smart zoning strategies. In: Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing, pp. 18–21. ACM (2017)

    Google Scholar 

  6. Chen, L., Wang, S., Fan, W., Sun, J., Naoi, S.: Beyond human recognition: a CNN-based framework for handwritten character recognition. In: 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 695–699. IEEE (2015)

    Google Scholar 

  7. Ghods, V., Sohrabi, M.: Online Farsi handwritten character recognition using hidden Markov model. J. Comput. 11(2), 169–175 (2016)

    Article  Google Scholar 

  8. Jurafsky, D., Wooters, C., Segal, J., Stolcke, A., Fosler, E., Tajchaman, G., Morgan, N.: Using a stochastic context-free grammar as a language model for speech recognition. In: International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 189–192. IEEE (1995)

    Google Scholar 

  9. Lai, S., Jin, L., Yang, W.: Toward high-performance online HCCR: a CNN approach with DropDistortion, path signature and spatial stochastic max-pooling. Pattern Recogn. Lett. 89, 60–66 (2017)

    Article  Google Scholar 

  10. Lawgali, A.: A survey on Arabic character recognition. Int. J. Sig. Process. Image Process. Pattern Recogn. 8(2), 401–426 (2015)

    Google Scholar 

  11. Tian, S., Bhattacharya, U., Lu, S., Su, B., Wang, Q., Wei, X., Lu, Y., Tan, C.: Multilingual scene character recognition with co-occurrence of histogram of oriented gradients. Pattern Recogn. 51, 125–134 (2015)

    Article  Google Scholar 

  12. Santosh, K., Wendling, L.: Character recognition based on non-linear multi-projection profiles measure. Front. Comput. Sci. 9(5), 678–690 (2015)

    Article  Google Scholar 

  13. Wang, S., Mathew, A., Chen, Y., Xi, L., Ma, L., Lee, J.: Empirical analysis of support vector machine ensemble classifiers. Expert Syst. Appl. 36(3), 6466–6476 (2009)

    Article  Google Scholar 

  14. Fox, R., Hartmann, W.: Hand-written character recognition using layered abduction. In: Sobh, T., Elleithy, K. (eds.) Advances in Systems, Computing Sciences and Software Engineering, pp. 141–147. Springer, Dordrecht (2016)

    Google Scholar 

  15. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  16. https://www.nist.gov/itl/iad/image-group/emnist-dataset

  17. Amari, S., Wu, S.: Improving support vector machine classifiers by modifying kernel functions. Neural Netw. 12(6), 783–789 (1999)

    Article  Google Scholar 

  18. Ukkonen, E.: Algorithms for approximate string matching. Inf. Control 64(1–3), 100–118 (1985)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Northern Kentucky University, Highland Heights, KY, 41099, USA

    Richard Fox & Steven Brownfield

Authors
  1. Richard Fox
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Steven Brownfield
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Richard Fox .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fox, R., Brownfield, S. (2019). Applying Context to Handwritten Character Recognition. In: Silhavy, R. (eds) Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-19810-7_5

Download citation

Publish with us

Policies and ethics

Search

Navigation