Skip to main content
SpringerLink
Log in

A case study on mathematical expression recognition to GPU

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The technology of mathematical expression identification and recognition extracts mathematical expressions in document images, and it has been studied for over a decade. Based on previous works, we develop an automatic recognition tool, named EqnEye, which leverages the OpenCV library to perform image processing and Tesseract tool to recognize mathematical symbols. We also apply correction methods before the recognition stage to improve the recognition accuracy. To improve the efficiency for processing images of high resolution, the parallel implementation of thresholding method on GPU is integrated into this work. Experimental results exhibit the success of our correction methods to enhance the accuracy and the slight improvement to the performance. In addition, porting the recognition tool to handy devices can produce more value-added applications.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. OpenCV, http://opencv.org/

  2. tesseract-ocr, https://github.com/tesseract-ocr

  3. Farber R (2011) CUDA application design and development. Morgan Kaufmann, Waltham, MA

  4. Sandes E, de Melo A (2013) Retrieving Smith-Waterman alignments with optimizations for megabase biological sequences using GPU. IEEE Trans Parallel Distrib Syst 24(5):1009–1021

    Article  Google Scholar 

  5. Zhu X, Li K, Salah A, Shi L, Li K (2015) Parallel implementation of MAFFT on CUDA-enabled graphics hardware. IEEE/ACM Trans Comput Biol Bioinform 12(1):205–218

    Article  Google Scholar 

  6. NVIDIA CUDA TOOLKIT 7.5, http://developer.download.nvidia.com/

  7. Chan K-F, Yeung D-Y (2000) Mathematical expression recognition: a survey. Int J Doc Anal Recognit 3(1):3–15

    Article  MathSciNet  Google Scholar 

  8. Zanibbi R, Blostein D, Cordy JR (2002) Recognizing mathematical expressions using tree transformation. IEEE Trans Pattern Anal Mach Intell 24(11):1455–1467

    Article  Google Scholar 

  9. Raja A et al (2006) Towards a parser for mathematical formula recognition. In: Mathematical knowledge management, LANI, vol 4108, pp 139–151

  10. Álvaro F, Sánchez JA (2010) Comparing several techniques for offline recognition of printed mathematical symbols. In: 20th International Conference on Pattern Recognition (ICPR), Istanbul

  11. Guo Y-S, Huang L, Liu C-P(2007) A new approach for understanding of structure of printed mathematical expression. In: International Conference on Machine Learning and Cybernetics, Hong Kong

  12. Lin X et al (2013) A text line detection method for mathematical formula recognition. In: 12th International Conference on Document Analysis and Recognition (ICDAR), Washington, DC

  13. Chu W-T, Liu F (2013) Mathematical formula detection in heterogeneous document images. In: Conference on Technologies and Applications of Artificial Intelligence (TAAI), Taipei

  14. Kumar PP, Agarwal A, Bhagvati C (2014) A knowledge-based design for structural analysis of printed mathematical expressions. In: 8th International Workshop on Multi-disciplinary Trends in Artificial Intelligence, LNCS, vol. 8875, pp 112–123

  15. Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–168

    Article  Google Scholar 

  16. Parker JR (2010) Algorithms for image processing and computer vision, 2nd edn. Wiley, Hoboken, NJ

  17. Singh BM, Sharma R, Mittal A, Ghosh D (2010) Parallel implementation of Otsu’s binarization approach on GPU. Int J Comput Appl 32(2):16–21

    Google Scholar 

  18. Li S, Shen Q, Sun J (2007) Skew detection using wavelet decomposition and projection profile analysis. Pattern Recognit Lett 28:555–562

    Article  Google Scholar 

Download references

Acknowledgments

This work is partially supported by the Ministry of Science and Technology under contract number MOST 104-2633-S-035-001.

Author information

Authors and Affiliations

  1. Department of Applied Mathematics, Feng Chia University, Taichung, Taiwan

    Ying-Chin Lin & Jing-Yun Zeng

  2. Industrial Ph.D. Program of Internet of Things, Feng Chia University, Taichung, Taiwan

    Ying-Chin Lin & Chun-Yao Wang

Authors
  1. Ying-Chin Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chun-Yao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing-Yun Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying-Chin Lin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, YC., Wang, CY. & Zeng, JY. A case study on mathematical expression recognition to GPU. J Supercomput 73, 3333–3343 (2017). https://doi.org/10.1007/s11227-016-1819-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-016-1819-3

Keywords

Search

Navigation