Movatterモバイル変換

[0]ホーム
URL:

Skip to main content
Springer Nature Link
Log in

Context-aware television-internet mash-ups using logo detection and character recognition

  • Industrial and Commercial Application
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Television can be a prime candidate for bringing internet to masses in an affordable manner, especially in developing nations. One such system available is called home infotainment platform (HIP) that uses an over-the-top box to provide a low-cost and affordable solution. However, user study from HIP suggests that the user experience of browsing internet on TV in a traditional way is not satisfactory. In this paper, we introduce the novel concept of context-aware television implemented on HIP, where we extract TV program contexts like identity and content using image processing techniques of logo detection and character recognition. There can be innovative internet-TV mash-up applications using such contexts. The techniques are especially useful for deriving the contexts from analog broadcast TV content that is prevalent in countries like India. The algorithms are designed in a lightweight manner so that they can be run efficiently on a low-cost resource-constrained platform like HIP. Experimental results with live Indian TV channel data show acceptable accuracy for the proposed systems with low-computational complexity.

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
¥17,985 /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 includes VAT (Japan)

Instant access to the full article PDF.

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

Notes

References

  1. ITU-T Technical Report (2011) Access to internet-sourced contents. HSTP-IPTV-AISC (2011–03), March 2011

  2. Fink M, Covell M, Baluja S (2006) Social- and interactive-television applications based on real-time ambient-audio identification. In: Proceedings of EuroITV.

  3. Baluja S, Covell M (2006) Content fingerprinting using wavelets, 3rd european conference on visual media production. (CVMP 2006), London

  4. Baluja S, Covell M (2008) Waveprint: efficient wavelet-based audio fingerprinting. Pattern Recognition, 41(11), Elsevier

  5. Chattopadhyay T, and Agnuru C (2010) Generation of electronic program guide for RF fed TV channels by recognizing the channel logo using fuzzy multifactor analysis. In: International symposium on consumer electronics (ISCE 2010), Germany

  6. Esen E, Soysal M, Ates TK, Saracoglu A, Alatan AA (2008) A fast method for animated TV logo detection. CBMI, June 2008

  7. Ekin A, Braspenning E (2006) Spatial detection of TV channel logos as outliers from the content. In: Proc VCIP, SPIE 2006

  8. Wang J, Duan L, Li Z, Liu J, Lu H, Jin JS (2006) A robust method for TV logo tracking in video streams. ICME, 2006

  9. Ozay N, Sankur B (2009) Automatic TV logo detection and classification in broadcast videos. EUSIPCO, Scotland, 2009

  10. Skolnik IM (2002) Introduction to radar systems, 3rd edn. McGraw-Hill, New York

    Google Scholar 

  11. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybernetics 9:1

  12. Harry McCracken (2009) The connected TV: web video comes to the living room. PC World, Mar 23, 2009

  13. Jung K, Kim KI, Jain AK (2004) Text information extraction in images and video: a survey. Pattern Recognition, Vol. 37, Issue 5, May 2004

  14. Shivakumara P, Trung QP, Chew LT (2009) A gradient difference based technique for video text detection. In: Proceedings of 10th international conference on document analysis and recognition, 26–29 July 2009

  15. Shivakumara P, Phan TQ, Lim TC (2009) A robust wavelet transform based technique for video text detection. In: Proceedings of 10th international conference on document analysis and recognition, 26–29 July 2009

  16. Emmanouilidis C, Batsalas C, Papamarkos N (2009) Development and evaluation of text localization techniques based on structural texture features and neural classifiers. In: Proceedings of 10th international conference on document analysis and recognition, 26–29, pp 1270–1274

  17. Jun Y, Lin-Lin H, Xiao LH (2009) Neural network based text detection in videos using local binary patterns. In: Proceedings of Chinese conference on pattern recognition, 4–6 Nov 2009, pp 1–5

  18. Zhong J, Jian W, Yu-Ting S (2009) Text detection in video frames using hybrid features. In: Proceedings of international conference on machine learning and cybernetics, pp 12–15

  19. Ngo CW, Chan CK (2005) Video text detection and segmentation for optical character recognition. Multimed Syst 10:3

  20. Anthimopoulos M, Gatos B, Pratikakis I (2008) A Hybrid system for text detection in video frames. In: Proceedings of the eighth IAPR international workshop on document analysis systems, pp 16–19

  21. Shivakumara P, Phan TQ, Lim TC (2009) Video text detection based on filters and edge features. In: Proceedings of IEEE international conference on multimedia and expo, June 28–July 3 2009

  22. Shivakumara P, Phan TQ, Lim TC (2008) Efficient video text detection using edge features. In: Proceedings of 19th international conference on pattern recognition, 8–11 Dec 2008

  23. Shivakumara P, Phan TQ, Lim TC (2008) An efficient edge based technique for text detection in video Frames. In: Proceedings of the eighth IAPR international workshop on document analysis systems, 16–19 Sept 2008

  24. Yu S, Wenhong W (2009) Text localization and detection for news video. In: Proceedings of second international conference on information and computing science, 21–22 May 2009

  25. Su Y, Ji Z, Song X, Hua R(2008) Caption text location with combined features using SVM. In: Proceedings of 11th IEEE international conference on communication technology, 10–12 Nov 2008

  26. Su Y, Ji Z, Song X, Hua R (2008) Caption text location with combined features for news videos. In: Proceedings of international workshop on geoscience and remote sensing and education technology and training, 21–22 Dec 2008

  27. Chattopadhyay T, Sinha A (2009) Recognition of trademarks from sports videos for channel hyperlinking in consumer end. In: Proceeding of the 13th international symposium on consumer electronics (ISCE’09), Japan, 25–28 May 2009

  28. Chattopadhyay T, Chaki A (2010) Identification of trademarks painted on ground and billboards using compressed domain features of H.264 from sports videos. National Conference on Computer Vision Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Jaipur, India, Jan 2010

  29. Indian Market Research Bureau (2010) I-Cube 2009–10, Feb 2010

  30. International Telecommunication Union (ITU) (2011) Measuring the information society

  31. Internet and Mobile Association of India (2011) Report on Mobile internet in India, Aug 2011

  32. International Telecommunication Union (ITU) (2011) The World in 2011––ICT facts and figures

  33. International Telecommunication Union (ITU) (2011), Information society statistical profiles–Asia and the Pacific

  34. Pal A, Prashant M, Ghose A, Bhaumik C (2010) Home infotainment platform—a ubiquitous access device for masses. In: Springer communications in computer and information science, Vol 75, 2010. Ubiquitous computing and multimedia applications, Also In: Proceedings on ubiquitous computing and multimedia applications (UCMA), Miyazaki, Japan, March 2010, pp 11–19

  35. Chattopadhyay T, Sinha A, Pal A, Pradhan D, Chowdhury SR (2011) Recognition of channel logos from streamed videos for value added services in connected TV. IEEE international conference for consumer electronics (ICCE), Las Vegas, USA

  36. Chattopadhyay T, Sinha A, Pal A (2011) TV Video context extraction. IEEE trends and developments in converging technology towards 2020 (TENCON 2011), Bali, Indonesia, Nov 21–24 2011

  37. Chattopadhyay T, Pal A, Garain U (2010) Mash up of breaking news and contextual web information: a novel service for connected television. In: Proceedings of 19th international conference on computer communications and networks (ICCCN), Zurich, Switzerland, Aug 2010

  38. Pal A, Sinha A, Chattopadhyay T (2010) Recognition of characters from streaming videos. In: Minoru M (ed) Character recognition, Sciyo Publications, ISBN: 978-953-307-105-3, Sept 2010

  39. Chattopadhyay T, Chaki A, Chattopadhayay D, Nandini Chatterjee, Pal A (2009) A novel value added interactive services for active pages of DTH set top boxes. Presented in experience workshop in third international conference on pattern recognition and machine intelligence (PReMI), New Delhi, India, Dec 2009

  40. Chattopadhyay T, Agnuru C (2010) Generation of electronic program guide for RF fed TV channels by recognizing the channel logo using fuzzy multifactor analysis. In: Proceedings of the 14th international symposium on consumer electronics (ISCE’10), Germany, 7–10 June 2010

  41. Chowdhury S, Garain U, Chattopadhyay T (2011) A weighted finite-state transducer (WFST)-based language model for online indic script handwriting recognition. In: Proceedings of 11th international conference on document analysis and recognition (ICDAR), Beijing, China, Sept 2011

  42. Chattopadhyay T, Sengupta S, Sinha A, Rampuria N (2011) Creation and analysis of a corpus of text rich Indian TV videos. In: Proceedings of 11th international conference on document analysis and recognition (ICDAR), Beijing, China, Sept 2011

  43. Chattopadhyay T, Chaudhuri BB, Jain R (2012) A novel low complexity TV video OCR system. In: 21st international conference on pattern recognition (ICPR), Japan, Nov 2012

  44. Pal A, Prasad R, Gupta R (2012) A low-cost connected tv platform for emerging markets—requirement analysis through user study. In: ESTIJ, Dec 2012

Download references

Acknowledgments

The authors thank Prof. Bidyut Baran Chaudhuri and Prof. Utpal Garain from Indian Statistical Institute for their kind advice and suggestions on the algorithm development. The authors also thank Chirabrata Bhaumik and Avik Ghose of TCS Innovation Labs for their help in system implementation of the proposed work on HIP. This work was supported by Innovation Lab, Tata Consultancy Services.

Author information

Authors and Affiliations

  1. Innovation Lab, Tata Consultancy Services, Kolkata, India

    Arpan Pal, Tanushyam Chattopadhyay & Aniruddha Sinha

  2. CTIF, Aalborg University, Aalborg, Denmark

    Ramjee Prasad

Authors
  1. Arpan Pal

    You can also search for this author inPubMed Google Scholar

  2. Tanushyam Chattopadhyay

    You can also search for this author inPubMed Google Scholar

  3. Aniruddha Sinha

    You can also search for this author inPubMed Google Scholar

  4. Ramjee Prasad

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toArpan Pal.

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pal, A., Chattopadhyay, T., Sinha, A.et al. Context-aware television-internet mash-ups using logo detection and character recognition.Pattern Anal Applic18, 191–205 (2015). https://doi.org/10.1007/s10044-014-0422-6

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /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 includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp