Skip to main content
Springer Nature Link
Log in

Robust handwriting extraction and lecture video summarization

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

In e-Learning research, teachers can record lecture videos in e-class and upload these lecture videos to e-Learning system themselves. Once lecture videos and handouts can be generated automatically in traditional classroom, it can help students with self-learning and teacher with lecture content development for e-Learning services. This paper proposed a teaching assistant system based on computer vision that can help in content development for e-Learning services. Lecture videos are taken by using two cameras and merged on both sides so that students can see a clear and complete teaching content. The k-means segmentation is used to extract board area and then connected component technique helps refill the board area which is covered by lecturer’s body. Then we use adaptive threshold to extract handwritings in various light conditions and time-series denoising technique is designed to reduce noise. According to extracted handwritings, the lecture videos can be automatically structured with high level of semantics. The lecture videos are segmented into video clips and all key-frames are integrated as handouts of the education videos.

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

Access this article

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

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Bay H, Tuytelaars T, Gool LV (2008) SURF: speeded up robust features. Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  2. Bhogal AK, Singla N, Kaur M (2010) Color image segmentation using K-means clustering algorithm. 1(2):18–20

  3. Brown M, Lowe DG (2007) Automatic panoramic image stitching using invariant features. Int J Comput Vis 74(1):59–73

    Article  Google Scholar 

  4. Chang HS, Sull S, Lee SU (1999) Efficient video indexing scheme for content-based retrieval. IEEE Trans Circ Syst Video Technol 9(8):1269–1279

    Article  Google Scholar 

  5. Choudary C, Liu T (2007) Summarization of visual content in instructional videos. IEEE Trans Multimed 9(7):1443–1455

    Article  Google Scholar 

  6. Ferman AM, Tekalp AM, Mehrotra R (2002) Robust color histogram descriptors for video segment retrieval and identification. IEEE Trans Image Process 11(5):497–508

    Article  Google Scholar 

  7. Fink GA, Wienecke M, Plötz T (2005) Experiments in video-based whiteboard reading. Proceedings of International Workshop on Camera-Based Document Analysis and Recognition, pp. 95–100

  8. Hartley R, Zisserman A (2000) Multiple view geometry in computer vision. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  9. He L, Zhang Z (2007) Real-time whiteboard capture and processing using a video camera for remote collaboration. IEEE Trans Multimed 9(1):198–206

    Article  Google Scholar 

  10. Hirzallah N, Nusir S, Al Sayyed A, Kayed A (2008) Notes extraction algorithm from traditional presentations without the use of e-boards. Proceedings of the International Conference on Computer and Communication Engineering, pp. 195–200

  11. Imran AS, Cheikh FA (2011) Blackboard content classification for lecture videos. Proceedings of International Conference on Image Processing, pp. 2989–2992

  12. Imran AS, Cheikh FA (2012) Lecture content classification tool. Proceedings of International Symposium on Communications Control and Signal Processing, pp. 1–6

  13. Imran AS, Rahadianti L, Cheikh FA, Yayilgan SY (2012) Semantic tags for lecture videos. Proceedings of International Conference on Semantic Computing, pp. 117–120

  14. Jain A (1986) Fundamentals of digital image processing. Prentice-Hall

  15. Lei Z, Chou W, Zhong J, Lee CH (2000) Video segmentation using spatial and temporal statistical analysis method. Proc Int Conf Multimed Expo 3:1527–1530

    Article  Google Scholar 

  16. Lin C, Sheu M, Chiang H, Liaw C, Tsai C (2005) An efficient video de-interlacing with scene change detection. Proceedings of the International Conference on Information, Communications and Signal Processing, pp. 36–40

  17. Liu TT, Choudary C (2006) Content Extraction and Summarization of Instructional Videos”, Proceedings of International Conference on Image Processing, pp. 149–152

  18. Okuni S, Tsuruoka S, Rayat GP, Kawanaka H, Shinogi T (2007) Video scene segmentation using the state recognition of blackboard for blended learning. Proceedings of International Conference on Convergence Information Technology, pp. 2437–2442

  19. Onishi M, Izumi M, Fukunaga K (2000) Blackboard segmentation using video image of lecture and its applications. Proceedings of International Conference on Pattern Recognition, pp. 615–618

  20. Saez E, Benavides JI, Guil N (2004) Reliable real time scene change detection in MPEG compressed video. Proc Int Conf Multimed Expo 1:567–570

    Google Scholar 

  21. Saund E (1999) Image mosaicing and a diagrammatic user interface for an office whiteboard scanner. Technical report, Xerox Palo Alto Research Center

  22. Zhang Z, He LW (2004) Notetaking with a camera: whiteboard scanning and image enhancement. Proc IEEE Int Conf Acoust Speech Signal Process 3:533–536

    Google Scholar 

  23. Zhang HJ, Kankanhalli A, Smoliar SW (1993) Automatic partitioning of full-motion video. Multimedia Systems 1(1):10–28

    Article  Google Scholar 

  24. Zhang D, Qi W, Zhang HJ (2001) A new shot boundary detection algorithm. Proceedings of the Second Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing, pp. 63–70

  25. Zhao L, Qi W, Li SZ, Yang SQ, Zhang HJ (2001) Content-based retrieval of video shot using the-improved nearest feature line method. Proc Int Conf Acoust Speech Signal Process 3:1625–1628

    Google Scholar 

  26. Zhou J, Zhang XP (2004) A web-enabled video indexing system. Proceedings of the International Workshop on Multimedia Information Retrieval, pp. 307–314

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan Normal University, No.88, Sec. 4, Tingzhou Rd., Wenshan Dist., Taipei City, Taiwan (116), China

    Greg C. Lee & Ying-Ju Chen

  2. Program of Information Technology, Fooyin University, No. 151, Chinhsueh Rd., Ta-liao, Kaohsiung, Taiwan, China

    Fu-Hao Yeh

  3. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan, China

    Tao-Ku Chang

Authors
  1. Greg C. Lee
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Fu-Hao Yeh
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ying-Ju Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Tao-Ku Chang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Fu-Hao Yeh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, G.C., Yeh, FH., Chen, YJ. et al. Robust handwriting extraction and lecture video summarization. Multimed Tools Appl 76, 7067–7085 (2017). https://doi.org/10.1007/s11042-016-3353-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3353-y

Keywords

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy