Skip to main content
SpringerLink
Log in

Research on Identification of Handwritten Mathematical Formulas

  • Conference paper
  • First Online:
International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019 (ATCI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1017))

Abstract

At present, handwriting recognition is making rapid progress, and accurate recognition of complex formulas has always been a difficulty, among which mathematical formulas are one of the representative examples. Due to the two-dimensional distribution of mathematical formulas, how to improve their accuracy has been a hot topic in handwritten recognition research. The research direction of this paper is to establish a structural system for better recognition of complex formulas in images, which is suitable for offline image recognition. The first part of this paper summarizes the methods of mathematical expression recognition and the application of various grammars. The second part introduces the system designed in this paper, including grammar, participle hypothesis generator, parsing algorithm and spatial relations.

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. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  2. Hong, Z.Q.: Algebraic feature extraction of image for recognition. Pattern Recogn. 24(3), 211–219 (1991)

    Article  MathSciNet  Google Scholar 

  3. Keysers, D., Deselaers, T., Gollan, C., Ney, H.: Deformation models for image recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1422–1435 (2007)

    Article  Google Scholar 

  4. Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697–8710 (2018)

    Google Scholar 

  5. Voigtlaender, P., Doetsch, P., Ney, H.: Handwriting recognition with large multidimensional long short-term memory recurrent neural networks. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 228–233(2016)

    Google Scholar 

  6. Ashiquzzaman, A., Tushar, A.K.: Handwritten Arabic numeral recognition using deep learning neural networks. In: 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 1–4 (2017)

    Google Scholar 

  7. Keysers, D., Deselaers, T., Rowley, H.A., Wang, L.L., Carbune, V.: Multi-language online handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1180–1194 (2017)

    Article  Google Scholar 

  8. Ahmed, S.B., Naz, S., Swati, S., Razzak, M.I.: Handwritten Urdu character recognition using one-dimensional BLSTM classifier. Neural Comput. Appl. 31, 1143–1151 (2019)

    Article  Google Scholar 

  9. Subramanian, K.G., Geethalakshmi, M., Nagar, A.K., Lee, S.K.: Two-dimensional picture grammar models. IN: 2008 Second UKSIM European Symposium on Computer Modeling and Simulation, Liverpool, pp. 263–267 (2008)

    Google Scholar 

  10. Lu, X., Lin, J.: Structure analysis algorithm of handwritten mathematical formula based on block tree. Comput. Eng. Sci. 32(10), 69–72 (2010)

    Google Scholar 

  11. Ren, K., Dai, Y., Wang, Q., Li, X., Yu, S.: Structural analysis of handwritten mathematical formulas in context perception. J. Nat. Sci. Xiangtan Univ. 36(02), 85–91 (2014)

    Google Scholar 

  12. Wu, B.: Research on two-dimensional grammar and its application in online handwritten mathematical formula recognition. South China University of Technology (2014)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Talent Project of Sichuan University of Science & Engineering (2017RCL23); the Science Founding of Artificial Intelligence Key Laboratory of Sichuan Province (2017RZJ03); the Opening Project of Sichuan Province University Key Laboratory of Bridge Non-destruction Detecting and Engineering Computing (2017QYJ03).

Author information

Authors and Affiliations

  1. Artificial Intelligence Key Laboratory of Sichuan Province, School of Science, Sichuan University of Science and Engineering, Zigong, People’s Republic of China

    Yu Cao, Zouyu Xie & Liufen Li

Authors
  1. Yu Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zouyu Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liufen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liufen Li .

Editor information

Editors and Affiliations

  1. Faculty of Science, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. School of Computing and Mathematics, Charles Sturt University, Albury, NSW, Australia

    Rafiqul Islam

  4. Shanghai University, Shanghai, China

    Zheng Xu

  5. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cao, Y., Xie, Z., Li, L. (2020). Research on Identification of Handwritten Mathematical Formulas. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019. ATCI 2019. Advances in Intelligent Systems and Computing, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-25128-4_183

Download citation

Publish with us

Policies and ethics

Search

Navigation