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SPODS: A Dataset of Color-Official Documents and Detection of Logo, Stamp, and Signature

  • Amit Vijay NandedkarEmail author
  • Jayanta Mukherjee
  • Shamik Sural
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10481)

Abstract

Office automation is an active area of research. It involves archival and retrieval of official documents. For developing a system for this purpose, it is necessary to have an extensive benchmark dataset consisting various types of official documents. However, it is hard to make available real world official documents as they are mostly confidential. In the absence of such benchmark datasets, it is difficult to evaluate newly developed algorithms. Hence, efforts have been made to build dataset consisting of different categories of documents that resemble real world official documents. In this work, we present a dataset called as scanned pseudo-official data-set (SPODS) which is created by us and made available online. Official documents are usually distinguished by presence of logo, stamp, signature, date, etc. The paper also presents a new approach for the detection of logo, stamp, and signature using spectral filtering and part based features. A comparative study on performances of the proposed method and existing algorithms on the SPODS dataset demonstrates the effectiveness of the proposed technique.

Keywords

Document analysis Graphics recognition Document understanding 

Notes

Acknowledgments

This work is partially sponsored by the Ministry of Communications & Information Technology, Govt. of India; Ref.: MCIT 11(19)/2010-HCC (TDIL) dt. 28-12-2010.

References

  1. 1.
    CBIR benchmark databases. http://savvash.blogspot.in/2008/12/benchmark-databases-for-cbir.html. Accessed 11 Jan 2016
  2. 2.
    Tobacco 800 dataset. http://www.umiacs.umd.edu/~zhugy/tobacco800.html. Accessed 7 Dec 2015
  3. 3.
    Ahmed, S., Malik, M.I., Liwicki, M., Dengel, A.: Signature segmentation from document images. In: International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 425–429. IEEE (2012)Google Scholar
  4. 4.
    Ahmed, S., Shafait, F., Liwicki, M., Dengel, A.: A generic method for stamp segmentation using part-based features. In: Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 708–712. IEEE (2013)Google Scholar
  5. 5.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  6. 6.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  7. 7.
    Dey, S., Mukherjee, J., Sural, S.: Logo and stamp detection from document images by finding outliers. In: Proceedings of the 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE (2015)Google Scholar
  8. 8.
    Dey, S., Mukherjee, J., Sural, S., Bhowmick, P.: Colored rubber stamp removal from document images. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds.) PReMI 2013. LNCS, vol. 8251, pp. 545–550. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-45062-4_75 CrossRefGoogle Scholar
  9. 9.
    Doermann, D., Tombre, K., et al.: Handbook of Document Image Processing and Recognition. Springer, London (2014)CrossRefzbMATHGoogle Scholar
  10. 10.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Hoboken (2012)zbMATHGoogle Scholar
  11. 11.
    Jain, R., Doermann, D.: Logo retrieval in document images. In: Proceedings of the 10th IAPR International Workshop on Document Analysis Systems, pp. 135–139. IEEE (2012)Google Scholar
  12. 12.
    Le, V.P., Nayef, N., Visani, M., Ogier, J.M., De Tran, C.: Document retrieval based on logo spotting using key-point matching. In: Proceedings of the 22nd International Conference on Pattern Recognition (ICPR), pp. 3056–3061. IEEE (2014)Google Scholar
  13. 13.
    Liu, L., Yu, M., Shao, L.: Multiview alignment hashing for efficient image search. IEEE Trans. Image Process. 24(3), 956–966 (2015)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Mandal, R., Roy, P.P., Pal, U.: Signature segmentation from machine printed documents using conditional random field. In: Proceedings of the 11th International Conference on Document Analysis and Recognition (ICDAR), pp. 1170–1174. IEEE (2011)Google Scholar
  15. 15.
    Micenková, B., van Beusekom, J.: Stamp detection in color document images. In: Proceedings of the 11th International Conference on Document Analysis and Recognition (ICDAR), pp. 1125–1129. IEEE (2011)Google Scholar
  16. 16.
    Nandedkar, A.V., Mukhopadhyay, J., Sural, S.: Text-graphics separation to detect logo and stamp from color document images: a spectral approach. In: Proceedings of the 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 571–575. IEEE (2015)Google Scholar
  17. 17.
    Roy, P.P., Pal, U., Lladós, J.: Document seal detection using GHT and character proximity graphs. Pattern Recogn. 44(6), 1282–1295 (2011)CrossRefGoogle Scholar
  18. 18.
    Rusiñol, M., Lladós, J.: Efficient logo retrieval through hashing shape context descriptors. In: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, pp. 215–222. ACM (2010)Google Scholar
  19. 19.
    Smeulders, A.W., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  20. 20.
    Srihari, S.N., Shetty, S., Chen, S., Srinivasan, H., Huang, C., Agam, G., Frieder, O.: Document image retrieval using signatures as queries. In: Proceedings of the 2nd International Conference on Document Image Analysis for Libraries (DIAL), pp. 198–203. IEEE (2006)Google Scholar
  21. 21.
    Wang, H., Chen, Y.: Logo detection in document images based on boundary extension of feature rectangles. In: Proceedings of the 10th International Conference on Document Analysis and Recognition (ICDAR), pp. 1335–1339. IEEE (2009)Google Scholar
  22. 22.
    Wong, K.Y., Casey, R.G., Wahl, F.M.: Document analysis system. IBM J. Res. Dev. 26(6), 647–656 (1982)CrossRefGoogle Scholar
  23. 23.
    Zhu, G., Doermann, D.: Automatic document logo detection. In: Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR), vol. 2, pp. 864–868. IEEE (2007)Google Scholar
  24. 24.
    Zhu, G., Zheng, Y., Doermann, D., Jaeger, S.: Signature detection and matching for document image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2015–2031 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Amit Vijay Nandedkar
    • 1
    Email author
  • Jayanta Mukherjee
    • 1
  • Shamik Sural
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of TechnologyKharagpurIndia

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