Advertisement

A Text Recognition Augmented Deep Learning Approach for Logo Identification

  • Moushumi MedhiEmail author
  • Shubham Sinha
  • Rajiv Ranjan Sahay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10481)

Abstract

Logo/brand name detection and recognition in unstructured and highly unpredictable natural images has always been a challenging problem. We notice that in most natural images logos are accompanied with associated text. Therefore, we address the problem of logo recognition by first detecting and isolating text of varying color, font size and orientation in the input image using affine invariant maximally stable extremal regions (MSERs). Using an off-the-shelf OCR, we identify the text associated with the logo image. Then an effective grouping technique is employed to combine the remaining stable regions based on spatial proximity of MSERs. Deep learning has the advantage that optimal features can be learned automatically from image pixel data. This motivates us to feed the clustered logo candidate image regions to a pre-trained deep convolutional neural network (DCNN) to generate a set of complex features which are further input to a multiclass support vector machine (SVM) for classification. We tested our proposed logo recognition system on 32 logo classes, and a non-logo class obtained by combining FlickrLogos-32 and MICC logo databases, amounting to a total of 23582 training and testing images. Our method yields robust recognition performance, outperforming state-of-the-art techniques achieving 97.8% precision, 95.7% recall and 95.7% average accuracy on the combined MICC and FlickrLogos-32 datasets and a precision of 98.6%, recall of 97.9% and average accuracy of 99.6% on only the FlickrLogos-32 dataset.

Keywords

Logo detection Logo recognition DCNN MSER 

References

  1. 1.
    Alaei, A., Delalandre, M., Girard, N.: Logo detection using painting based representation and probability features. In: ICDAR, pp. 1235–1239 (2013)Google Scholar
  2. 2.
    Boia, R., Florea, C., Florea, L., Dogaru, R.: Logo localization and recognition in natural images using homographic class graphs. Mach. Vis. Appl. 27(2), 287–301 (2016)CrossRefGoogle Scholar
  3. 3.
    Romberg, S., Pueyo, L.G., Lienhart, R., Zwol, R.V.: Scalable logo recognition in real-world images. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval, pp. 965–968 (2011)Google Scholar
  4. 4.
    Chen, W., Lan, S., Xu, P.: Multiple feature fusion via hierarchical matching for TV logo recognition. In: Proceedings of the 8th International Congress on Image and Signal Processing, IEEE (2015)Google Scholar
  5. 5.
    Sahbi, H., Ballan, L., Serra, G., Bimbo, A.: Context-dependent logo matching and recognition. IEEE Trans. Image Process. 22(3), 1018–1031 (2013). IEEECrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Zhang, Y., Zhang, S., Liang, W., Guo, Q.: Individualized matching based on logo density for scalable logo recognition. In: ICASSP, pp. 4324–4328 (2014)Google Scholar
  7. 7.
    Hassanzadeh, S., Pourghassem, H.: Fast logo detection based on morphological features in document images. In: Proceedings of the 7th International Colloquium on Signal Processing and its Applications, pp. 283–286 (2011)Google Scholar
  8. 8.
    Hoi, S.C.H., Wu, X., Liu, H., Wu, Y., Wang, H., Xue, H., Wu, Q.: LOGO-net: largescale deep logo detection and brand recognition with deep region-based convolutional networks. arXiv:1511.02462 (2015)
  9. 9.
    Iandola, F.N., Shen, A., Gao, P., Keutzer, K.: DeepLogo: hitting logo recognition with the deep neural network hammer. arXiv:1510.02131 (2015)
  10. 10.
    Krizhevsky, A., Sutskever, I., Hinton, J.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 1097–1105 (2012)Google Scholar
  11. 11.
    Oliveira, G., Frazão, X., Pimentel, A., Ribeiro. B.: Automatic graphic logo detection via fast region-based convolutional networks. arXiv:1604.06083 (2016)
  12. 12.
    Uijlings, J.R.R., Van De Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. Intl. J. Comput. Vis. 104(2), 154–171 (2013). SpringerCrossRefGoogle Scholar
  13. 13.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2002)CrossRefGoogle Scholar
  14. 14.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). doi: 10.1007/978-3-319-10590-1_53 Google Scholar
  15. 15.
    Hancock, J.M.: Jaccard distance (Jaccard Index, Jaccard Similarity Coefficient). Dictionary Bioinform. Comput. Biol (2004)Google Scholar
  16. 16.
    Huang, W., Qiao, Y., Tang, X.: Robust scene text detection with convolution neural network induced MSER trees. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 497–511. Springer, Cham (2014). doi: 10.1007/978-3-319-10593-2_33 Google Scholar
  17. 17.
    de Campos, T.E., Babu, B.R., Varma, M.: Character recognition in natural images. In: Proceedings of the 4th International Conference on Computer Vision Theory and Applications, pp. 273–280 (2009)Google Scholar
  18. 18.
    Revaud, J., Douze, M., Schmid, C.: Correlation-based burstiness for logo retrieval. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 965–968 (2012)Google Scholar
  19. 19.
    Romberg, S., Lienhart, R.: Bundle min-hashing for logo recognition. In: Proceedings of the 3rd ACM Conf. on International Conference on Multimedia Accessed, pp. 113–120 (2013)Google Scholar
  20. 20.
    Farajzadeh, N.: Exemplar-based logo and trademark recognition. Mach. Vis. Appl. 26(6), 791–805 (2015)CrossRefGoogle Scholar
  21. 21.
    Liu, Y., Wang, J., Li, Z., Li, H.: Efficient logo recognition by local feature groups. Multimedia Syst. 1–9 (2016)Google Scholar
  22. 22.
    Nair, V., Hinton, G.: Rectified linear units improve restricted boltzmann machines. In: ICML (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Moushumi Medhi
    • 1
    Email author
  • Shubham Sinha
    • 2
  • Rajiv Ranjan Sahay
    • 3
  1. 1.Computational Vision Lab, Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia
  2. 2.Department of Computer Science and TechnologyIndian Institute of Engineering Science and TechnologyShibpurIndia
  3. 3.Department of Electrical EngineeringIndian Institute of Technology KharagpurKharagpurIndia

Personalised recommendations