Skip to main content
SpringerLink
Log in
Book cover

International Conference in Communications, Signal Processing, and Systems

CSPS 2019: Communications, Signal Processing, and Systems pp 75–82Cite as

Natural Scene Mongolian Text Detection Based on Convolutional Neural Network and MSER

  • Conference paper
  • First Online:

  • 89 Accesses

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 571))

Abstract

Maximum Stable Extreme Region (MSER) is the most influential algorithm in text detection. However, due to the complex and varied background of Mongolian text in natural scene images, it is difficult to distinguish between text and non-text connected regions, thus reducing the robustness of the MSER algorithm. Therefore, this paper proposes to extract the connected regions in the natural scene pictures by applying MSER, and then uses the convolutional neural network (CNN) to train a high-performance text classifier to classify the extracted connected regions, and finally obtaining the final detection results. This paper evaluates the proposed method on the CSIMU-MTR dataset established by the School of Computer Science, Inner Mongolia University. The recall rate is 0.75, the accuracy rate is 0.83, and the F-score is 0.79, which is significantly higher than the previous method. It shows the effectiveness of the proposed Mongolian text detection method for natural scenes.

This is a preview of subscription content, 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   629.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   799.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   799.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Gao G, Su X, Wei H et al (2011) Classical mongolian words recognition in historical document. In: International conference document analysis recognition, IEEE, pp 692–697

    Google Scholar 

  2. Wei H, Gao G (2014) A keyword retrieval system for historical mongolian document images. Int J Doc Anal Recognit (IJDAR) 17(1):33–45

    Article  Google Scholar 

  3. Ye Q, Doermann D (2015) Text detection and recognition in imagery: a survey. IEEE Trans Pattern Anal Mach Intell 37(7):1480–1500

    Article  Google Scholar 

  4. Jaderberg M, Vedaldi A, Zisserman A (2014) Deep features for text spotting. In: Computer vision—ECCV, pp 512–528

    Google Scholar 

  5. Chen X, Yuille AL (2004) Detecting and reading text in natural scenes. In: IEEE computer society conference on computer vision and pattern recognition, pp 366–373

    Google Scholar 

  6. Babenko B, Belongie S (2011) End-to-end scene text recognition. In: IEEE international conference on computer vision, pp 1457–1464

    Google Scholar 

  7. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE Comput Soc Conf Comput Vision Pattern Recognit 1:886–893

    Google Scholar 

  8. Chen H, Tsai SS, Schroth G et al (2011) Robust text detection in natural images with edge-enhanced maximally stable extremal regions. In: 18th IEEE international conference on image processing, pp 2609–2612

    Google Scholar 

  9. Yin XC, Yin X, Huang K et al (2014) Robust text detection in natural scene images. IEEE Trans Pattern Anal Mach Intell 36(5):970–983

    Article  Google Scholar 

  10. He T, Huang W, Qiao Y et al (2015) Text-attentional convolutional neural networks for scene text detection. IEEE Trans Image Process 25(6):2529–2541

    Article  MathSciNet  Google Scholar 

  11. Epshtein B, Ofek E, Wexler Y (2010) Detecting text in natural scenes with stroke width transform. In: IEEE computer society conference on computer vision and pattern recognition, pp 2963–2970

    Google Scholar 

  12. Nistér D, Stewénius H (2008) Linear time maximally stable extremal regions. In: European conference on computer vision-ECCV, pp 183–196

    Google Scholar 

  13. Huang W, Qiao Y, Tang X (2014) Robust scene text detection with convolution neural network induced MSER trees. In: Computer vision–ECCV, pp 497–511

    Google Scholar 

  14. Neubeck A, Gool L (2006) Efficient non-maximum suppression. In: 18th ICPR

    Google Scholar 

  15. Shao Y, Wang C, Xiao B (2013) Fast self-generation voting for handwritten Chinese character recognition. Int J Doc Anal Recognit (IJDAR) 16(4):413–424

    Article  Google Scholar 

  16. Shao Y, Wang C, Xiao B (2015) A character image restoration method for unconstrained handwritten Chinese character recognition. Int J Doc Anal Recognit (IJDAR) 18(1):73–86

    Article  Google Scholar 

  17. Wang T, Wu DJ, Coates A, Ng AY (2012) End-to-end text recognition with convolutional neural network. In: IEEE international conference on pattern recognition, pp 3304–3308

    Google Scholar 

  18. He K, Zhang X, Ren S et al (2016) Identity mappings in deep residual networks

    Google Scholar 

  19. Shao Y, Gao G, Zhang L et al (2015) The first robust mongolian text reading dataset CSIMU-MTR, pp 781–788

    Google Scholar 

  20. Wolf C, Jolion JM (2006) Object count/area graphs for the evaluation of object detection and segmentation algorithms. Int J Doc Anal Recognit 8(4):280–296

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (NSFC) under Grant no. 61563039.

Author information

Authors and Affiliations

  1. College of Computer Science, Inner Mongolia University, Inner Mongolia, People’s Republic of China

    Yunxue Shao & Hongyu Suo

Authors
  1. Yunxue Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongyu Suo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunxue Shao .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  3. School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning, China

    Xin Liu

  4. School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China

    Zhenyu Na

  5. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China

    Min Jia

  6. College of Physical and Electronic Information, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shao, Y., Suo, H. (2020). Natural Scene Mongolian Text Detection Based on Convolutional Neural Network and MSER. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9409-6_10

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9408-9

  • Online ISBN: 978-981-13-9409-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation