Skip to main content
SpringerLink
Log in

CNN-Based Handwritten Mathematical Symbol Recognition Model

  • Conference paper
  • First Online:
Cyber Intelligence and Information Retrieval

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 291))

Abstract

Mathematical expression or formula recognition system is evolving field in pattern recognition. Various methods are implemented by different researchers on wide areas. Handwritten mathematical expression field evolved in last decades rapidly. This paper aims to target and represent CNN as an application of the deep learning method for recognizing the handwritten mathematical symbols. The current trend of the deep learning architectures witnesses CNN is one of the most prominent and widely used techniques that have been successfully implemented in NLP, computer vision, and pattern recognition. The kinds of deep learning models have proven to produce a recognizable state of the art. In this paper, we recognize the handwritten mathematical symbols using CNN. This paper comprises stages involved in recognition process, challenges and proposed methodology used by the author to conduct their experiment and further result and future scope is discussed. Dataset used for this experiment is downloaded from the public available platform (HasyV2 Dataset 2018. A dataset of size 369 classes comprising 168,223 images has been used for experimentation, and the results relieve the accuracy of 76.17%. For further improvement, an extra dense net (fully connected layer) has been used. The DuosdenseNet model has been designed; the accuracy of recognizable results shows a decent work for consideration.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Similar content being viewed by others

References

  1. HasyV2 Dataset (2018)

    Google Scholar 

  2. Kukreja V, Dhiman P (2020) A deep neural network based disease detection scheme for Citrus fruits. In: 2020 International conference on smart electronics and communication (ICOSEC), pp 97–101

    Google Scholar 

  3. Chan KF, Yeung DY (2000) Mathematical expression recognition: a survey. Int J Doc Anal Recogn (iJDAR) 3(1):3–15. https://doi.org/10.1007/PL00013549

  4. Hu L, Zanibbi R (2011) HMM-based recognition of online handwritten mathematical symbols using segmental K-means initialization and a modified pen-up/down feature. In: Proceedings of the international conference on document analysis and recognition, ICDAR, pp 457–462. https://doi.org/10.1109/ICDAR.2011.98

  5. Shinde S, Waghulade R (2016) Handwritten mathematical expressions recognition using back propagation artificial neural network. Commun Appl Electron 4(7):1–6. https://doi.org/10.5120/cae2016652125

    Article  Google Scholar 

  6. Huang Z, Dong M, Mao Q, Zhan Y (2014) Speech emotion recognition using CNN. In: Proceedings of the 22nd ACM international conference on multimedia, pp 801–804

    Google Scholar 

  7. Nguyen K, Fookes C, Ross A, Sridharan S (2017) Iris recognition with off-the-shelf CNN features: A deep learning perspective. IEEE Access 6:18848–18855

    Article  Google Scholar 

  8. Hossain MB, Naznin F, Joarder YA, Zahidul Islam M, Uddin MJ (2018) Recognition and solution for handwritten equation using convolutional neural network. In: Proceedings Jt. 7th international conference informatics, electronics vision 2nd international conference imaging, vision pattern recognition, ICIEV-IVPR, pp 250–255. https://doi.org/10.1109/ICIEV.2018.8640991

  9. Shuvo SN, Hasan F, Ahmed MU, Hossain SA, Abujar S (2020) MathNET: using CNN bangla handwritten digit, mathematical symbols, and trigonometric function recognition. In: Soft computing techniques and applications, Springer, pp 515–523

    Google Scholar 

  10. Julca-Aguilar FD, Hirata NST (2018) Symbol detection in online handwritten graphics using faster R-CNN. In: 2018 13th IAPR international workshop on document analysis systems (DAS), pp 151–156

    Google Scholar 

  11. Dai HAIN, Le Duc ANH, NAKAGAWA M (2014) Combination of LSTM and CNN for recognizing mathematical symbols. In: Proceedings of the 17th information-based induction sciences workshop

    Google Scholar 

  12. Drsouza L, Mascarenhas M (2018) Offline handwritten mathematical expression recognition using convolutional neural network. In: Proceedings of international conference on information, communication, engineering and technology, ICICET, pp 1–3. https://doi.org/10.1109/ICICET.2018.8533789

  13. Nguyen CT, Khuong VTM, Nguyen HT, Nakagawa M (2020) CNN based spatial classification features for clustering offline handwritten mathematical expressions. Pattern Recognit Lett 131:113–120. https://doi.org/10.1016/j.patrec.2019.12.015

    Article  Google Scholar 

  14. Kukreja V, Kumar D, Kaur A et al. (2020) GAN-based synthetic data augmentation for increased CNN performance in vehicle number plate recognition. In: 2020 4th international conference on electronics, communication and aerospace technology (ICECA), pp 1190–1195

    Google Scholar 

  15. Ciresan DC, Meier U, Gambardella LM, Schmidhuber J (2011) Convolutional neural network committees for handwritten character classification. In: 2011 International conference on document analysis and recognition, pp 1135–1139

    Google Scholar 

  16. Ahranjany SS, Razzazi F, Ghassemian MH (2010) A very high accuracy handwritten character recognition system for Farsi/Arabic digits using convolutional neural networks. In: 2010 IEEE fifth international conference on bio-inspired computing: theories and applications (BIC-TA), pp 1585–1592

    Google Scholar 

  17. Thoma M (2017) The hasyv2 dataset. arXiv Prepr. arXiv1701.08380

    Google Scholar 

  18. Hambal AM, Pei Z, Ishabailu FL (2017) Image noise reduction and filtering techniques. Int J Sci Res 6(3):2033–2038

    Google Scholar 

  19. Chauhan S, Sharma E, Doegar A et al. (2016) Binarization techniques for degraded document images—a review. In: 2016 5th international conference on reliability, infocom technologies and optimization (Trends and Future Directions)(ICRITO), pp 163–166

    Google Scholar 

  20. Zhang G, Ni G (2008) A new fast and effective recognition method based construction shape for printed digital. IJCSNS 8(5):309

    Google Scholar 

  21. Kaur D, Kaur Y (2014) Various image segmentation techniques: a review. Int J Comput Sci Mob Comput 3(5):809–814

    Google Scholar 

  22. Khokher MR, Ghafoor A, Siddiqui AM (2013) Image segmentation using multilevel graph cuts and graph development using fuzzy rule-based system. IET Image Process 7(3):201–211

    Article  MathSciNet  Google Scholar 

  23. Dey V, Zhang Y, Zhong M (2010) A review on image segmentation techniques with remote sensing perspective. vol 38. na Vienna, Austria

    Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Sakshi & Vinay Kukreja

  2. Chitkara University, Solan , Himachal Pradesh, India

    Chetan Sharma

Authors
  1. Sakshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chetan Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vinay Kukreja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chetan Sharma .

Editor information

Editors and Affiliations

  1. Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  2. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, India

    Paramartha Dutta

  3. Institute of Engineering and Management, Kolkata, India

    Soumi Dutta

  4. Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India

    Debabrata Samanta

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sakshi, Sharma, C., Kukreja, V. (2022). CNN-Based Handwritten Mathematical Symbol Recognition Model. In: Tavares, J.M.R.S., Dutta, P., Dutta, S., Samanta, D. (eds) Cyber Intelligence and Information Retrieval. Lecture Notes in Networks and Systems, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-16-4284-5_35

Download citation

Publish with us

Policies and ethics

Search

Navigation