Skip to main content
SpringerLink
Log in

Multi-lingual Indian Text Detector for Mobile Devices

  • Conference paper
  • First Online:
Computer Vision and Image Processing (CVIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1377))

Included in the following conference series:

Abstract

Detection of text in natural scene images is a challenging problem owing to its significant variations in the appearance of the texts as well as the background. The task becomes even more difficult for Indian texts, due to the presence of multiple languages and scripts. Most of the recent efficient schemes use very deep CNN which requires large memory and computation. In this paper, we proposed a multi-lingual text detection system based on the compressed versions of an object detection framework called YOLO (You Only Look Once) [13] namely, YOLO v3-Tiny and YOLO v4-Tiny. The aspect ratios of the anchor boxes are calculated using K-means clustering on the ground truth values, so that it can accurately detect words of varying lengths. The text detector has been evaluated on the IndicSceneText2017 [11] data set which consists of three Indian languages along with English. Experimental results prove the efficiency of the proposed scheme, for use in embedded systems and mobile devices due to its fast inference speed and small model size.

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
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. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  2. Chen, X., Yuille, A.L.: Detecting and reading text in natural scenes. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2004, CVPR 2004, vol. 2, p. II. IEEE (2004)

    Google Scholar 

  3. Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2963–2970. IEEE (2010)

    Google Scholar 

  4. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  5. Huang, L., Yang, Y., Deng, Y., Yu, Y.: DenseBox: unifying landmark localization with end to end object detection. arXiv preprint arXiv:1509.04874 (2015)

  6. Liao, M., Shi, B., Bai, X., Wang, X., Liu, W.: TextBoxes: a fast text detector with a single deep neural network. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  7. Liu, Y., Chen, H., Shen, C., He, T., Jin, L., Wang, L.: ABCNet: real-time scene text spotting with adaptive bezier-curve network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9809–9818 (2020)

    Google Scholar 

  8. Liu, Y., Jin, L., Zhang, S., Luo, C., Zhang, S.: Curved scene text detection via transverse and longitudinal sequence connection. Pattern Recogn. 90, 337–345 (2019)

    Article  Google Scholar 

  9. Ma, J., et al.: Arbitrary-oriented scene text detection via rotation proposals. IEEE Trans. Multimedia 20(11), 3111–3122 (2018)

    Article  Google Scholar 

  10. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image vision Comput. 22(10), 761–767 (2004)

    Article  Google Scholar 

  11. Mathew, M., Jain, M., Jawahar, C.V.: Benchmarking scene text recognition in Devanagari, Telugu and Malayalam. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 7, pp. 42–46. IEEE (2017)

    Google Scholar 

  12. Mishra, A., Alahari, K., Jawahar, C.V.: Top-down and bottom-up cues for scene text recognition. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2687–2694. IEEE (2012)

    Google Scholar 

  13. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  14. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

  15. Su, F., Xu, H.: Robust seed-based stroke width transform for text detection in natural images. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 916–920. IEEE (2015)

    Google Scholar 

  16. Tang, P., Yuan, Y., Fang, J., Zhao, Y.: A novel similar background components connection algorithm for colorful text detection in natural images. In: 2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1–5. IEEE (2015)

    Google Scholar 

  17. Tian, S., Pan, Y., Huang, C., Lu, S., Yu, K., Tan, C.L.: Text flow: a unified text detection system in natural scene images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4651–4659 (2015)

    Google Scholar 

  18. Tian, Z., Huang, W., He, T., He, P., Qiao, Yu.: Detecting text in natural image with connectionist text proposal network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 56–72. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_4

    Chapter  Google Scholar 

  19. Wang, C.-Y., Liao, H.-Y.M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., Yeh, I.-H.: CSPNet: a new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 390–391 (2020)

    Google Scholar 

  20. Wang, K., Babenko, B., Belongie, S.: End-to-end scene text recognition. In: 2011 International Conference on Computer Vision, pp. 1457–1464. IEEE (2011)

    Google Scholar 

  21. Xu, H., Xue, L., Su, F.: Scene text detection based on robust stroke width transform and deep belief network. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 195–209. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16808-1_14

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  23. Yin, X.-C., Yin, X., Huang, K., Hao, H.-W.: Robust text detection in natural scene images. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 970–983 (2013)

    Google Scholar 

  24. Zhou, X., et al.: East: an efficient and accurate scene text detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5551–5560 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology Guwahati, Guwahati, Assam, India

    Veronica Naosekpam, Naukesh Kumar & Nilkanta Sahu

Authors
  1. Veronica Naosekpam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naukesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nilkanta Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Veronica Naosekpam .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Prayagraj, India

    Satish Kumar Singh

  2. Indian Institute of Technology Roorkee, Roorkee, India

    Partha Roy

  3. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  4. Indian Institute of Information Technology Allahabad, Prayagraj, India

    P. Nagabhushan

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Naosekpam, V., Kumar, N., Sahu, N. (2021). Multi-lingual Indian Text Detector for Mobile Devices. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_21

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-1092-9_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1091-2

  • Online ISBN: 978-981-16-1092-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation