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Logo Retrieval and Document Classification Based on LBP Features

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Data Analytics and Learning

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 43))

Abstract

Logo is the strong entity for retrieval of Content-Based Information (CBI) from any complex document image. Logo is the primary and unique entity which is used to identify the ownership of the documents. Automatic logo detection and retrieval facilitates efficient identification of the source of the document and it is one of the interesting problems to the document retrieval community. Wes proposed a method based on Local Binary Pattern (LBP) for logo retrieval from document images. It is used to describe the logos both query and document logo. The candidate and query logos ares matched based on the cosine distance. Based on it, distance ranks are generated to estimate the relevance of the logo. Later, matched logos are retrieved at a selected threshold of 98%. The performance of the algorithm is experimentally validated and its efficiency is measured in terms of the mean precision at the rate 87.80%, and mean recall rate 88.20% as well as average F-measure 88.00%.

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References

  1. Kuo, S.-S., Agazzil, O.E.: Keyword spotting in poorly printed documents using pseudo 2-D hidden Markov models. In: IEEE, pp. 0162–8828 (1994)

    Google Scholar 

  2. The legacy tobacco document library (LTDL) at UCSF (2006). http://legacy.library.ucsf.edu/

  3. Doermann, D., Rivlin, E., Weiss, I.: Logo recognition using geometric invariants. In: Proceedings of International Conference on Document Analysis and Recognition, pp. 897–7 (1993)

    Google Scholar 

  4. Doermann, D., Rivlin, E., Weiss, I.: Applying and differential invariants for logo recognition. Mach. Vis. Appl. 9(2), 73–86 (1996)

    Google Scholar 

  5. Neumann, J., Samet, H., Soffer A.: Integration and global shape analysis for logo classification. Pattern Recognit. Lett. 23(12), 1449–1457 (2002)

    Google Scholar 

  6. Suda, P., Bridoux, C., Kammerer, B., Maderlechner, G.: Logo and word matching using a registration. In: Proceedings of International Conference on Document Analysis and Recognition, pp. 61–65 (1997)

    Google Scholar 

  7. Pham, T.: Unconstrained logo detection in document images. Pattern Recognit. 36(12), 3023–3025 (2003)

    Article  Google Scholar 

  8. Zhu, G., Doermann, D.: Automatic document logo detection. In: Conference on Document Analysis and Recognition, pp. 864–868 (2007)

    Google Scholar 

  9. Doermann, D., Rivlin, E., Weiss, I.: Logo recognition using geometric invariants. In: Proceedings International Conference on Document Analysis and Recognition, pp. 897–903 (1993)

    Google Scholar 

  10. Zhu, G., Doermann, D.: Logo matching for document image retrieval. In: ICDAR, pp. 606–610 (2009)

    Google Scholar 

  11. Neumann, J., Samet, H., Soffer, A.: Integration and global shape analysis for logo classification. Pattern Recognit. Lett. 23(12), 1449–1457 (2002)

    Article  Google Scholar 

  12. Rusinol, M., Liados, J.: Logo spotting by a bag-of-words approach for document categorization. In: Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), pp. 111–115 (2009)

    Google Scholar 

  13. Zhu, G., Doermann, D.: Logo matching for document image retrieval. In: Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), pp. 606–610 (2009)

    Google Scholar 

  14. Pham, T.: Unconstrained logo detection in document images. Pattern Recognit. 36(12), 2025–3023 (2003)

    Article  Google Scholar 

  15. Chen, J., Leung, M.K., Gao, Y.: Noisy logo recognition using line segment Hausdorff distance. Pattern Recognit. 36(4), 943–955 (2003)

    Article  Google Scholar 

  16. Zhu, G., Doermann, D.: Logo matching for document image retrieval. In: Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), pp. 606–610 (2009)

    Google Scholar 

  17. Dhandra, B.V., Hangarge, M..: On seperation of english numerals from multilingual document images. Int. J. Multimed. (JM) 2(6), 26–33 (2007). ISSN 1796-2048. Academy Publisher, Oulu, Finland

    Google Scholar 

  18. Otsu, N.: A threshold selection method from gray-level histograms. In: IEEE PAMI-79, pp. 62–66 (1979)

    Article  Google Scholar 

  19. Hangarge, M., Dhandra, B.V.: Script identification in indiandocument images based on directional morphological filters. Int. J. Recent Trends Eng. 2 (2009)

    Google Scholar 

  20. Ojala, T., Pietikainen, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  21. The IIT complex document image processing (CDIP) test collection (2006). http://ir.iit.edu/projects/CDIP.html

  22. Zhu, G., Doermann, D.: Automatic document logo detection. In: Conference on Document Analysis and Recognition. pp. 864–868 (2007)

    Google Scholar 

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Correspondence to C. Veershetty .

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Veershetty, C., Hangarge, M. (2019). Logo Retrieval and Document Classification Based on LBP Features. In: Nagabhushan, P., Guru, D., Shekar, B., Kumar, Y. (eds) Data Analytics and Learning. Lecture Notes in Networks and Systems, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-2514-4_12

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