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Transform-Based Text Detection Approach in Images

  • C. Naveena
  • B. N. Ajay
  • V. N. Manjunath Aradhya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 863)

Abstract

Nowadays, every document is very essential to be digitized. Increase in the gadgets where everyone likes to take the information in the form of images, but these images contains important information and necessary to be digitize. To do the digitization text detection in an image is one of the important stage in any field of document image analysis. But its not an easy task due to some of the challenges like complex background, varying light condition, low resolution etc. Hence, this work proposed detection of text in images. The proposed methodology consists of three steps. Initially the gabor filter is applied to extract the uncertainty features of the images. Then, 2D wavelet transform is applied to decompose the text information. Finally non-text information is removed using textual features based on the edge information. The proposed method is tested on MRRC and MSRA-TD500 standard dataset and obtained encouraging results.

Keywords

Text detection Gabor filter Wavelet transform Edge detection Localization 

References

  1. 1.
    Manjunath Aradhya, V.N., Pavithra, M.S.: A comprehensive of transforms, Gabor filter and k-means clustering for text detection in images and video. Appl. Comput. Inf. 12, 109–116 (2016)Google Scholar
  2. 2.
    Yan, J., Li, J., Gao, X.: Chinese text location under complex background using Gabor filter and SVM. Neurocomputing 74, 2998–3008 (2011)Google Scholar
  3. 3.
    Tadic, V., Popovic, M., Odry, P.: Fuzzified Gabor filter for license plate detection. Eng. Appl. Artif. Intell. 48, 2998–3008 (2016)Google Scholar
  4. 4.
    Zaafouri, A., Sayadi, M., Fnaiech, F., al Jarrah, O., Wei, W.: A new method for expiration code detection and recognition using Gabor features based collaborative representation. Adv. Eng. Inf. 29, 1072–1082 (2015)Google Scholar
  5. 5.
    Liang, G., Shivakumara, P., Lu, T., Tan, C.L.: A new wavelet-Laplacian method for arbitrarily-oriented character segmentation in video text lines. In: 13th International Conference on Document Analysis and Recognition (ICDAR), vol. 978, pp. 926–930 (2015)Google Scholar
  6. 6.
    Grzegorzek, M., Li, C., Raskatow, J., Paulus, D., Vassilieva, N.: Texture-based text detection in digital images with wavelet features and support vector machines. In: CORES 2013, vol. 226, pp. 857–866 (2013)Google Scholar
  7. 7.
    Manjunath Aradhya, V.N., Pavithra, M.S., Naveena, C.: A robust multilingual text detection approach based on transforms and wavelet entropy. C3IT-2012 4, 232–237 (2012)Google Scholar
  8. 8.
    Ali, S.A., Hashim, A.T.: Wavelet transform based technique for text image localization. Karbala Int. J. Mod. Sci. 2 (2016)Google Scholar
  9. 9.
    Al-Kadi, O.S.: Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques. Department of Informatics, University of Sussex, Brighton, Ph.D. thesis edition (2009)Google Scholar
  10. 10.
    Turner, M.R.: Texture-discrimination by Gabor functions. Biol. Cybern. 55, 71–82 (1986)Google Scholar
  11. 11.
    Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z.: Detecting texts of arbitrary orientations in natural images. In: CVPR, pp. 1083–1090 (2012)Google Scholar
  12. 12.
    Shivakumar, P., Basavaraju, H.T., Guru D.S., Tan, C.L.: Detection of curved text in video: quad tree based method. In: 12th International Conference on Document Analysis and Recognition(ICDAR), pp. 594–598 (2013)Google Scholar
  13. 13.
    Lu, C., Wang, C., Dai, R.: Text detection in images based on unsupervised classification of edge-based features. In: Proceedings of the ICDAR, pp. 610–614 (2005)Google Scholar
  14. 14.
    Zhao, X., Lin, K.H., Fu, Y., Hu, Y., Liu, Y., Huang, T.S.: Text from corners: a novel approach to detect text and caption in videos. IEEE Trans. IP 20, 790–799 (2011)Google Scholar
  15. 15.
    Kang, L., Li, Y., Doermann, D.: Orientation robust text line detection in natural images. In: Proceedings of the CVPR, pp. 4034–4041 (2014)Google Scholar
  16. 16.
    Yao, C., Bai, X., Liu, W.: A unified framework for multi-oriented text detection and recognition. IEEE Trans. IP 23, 4737–4749 (2014)Google Scholar
  17. 17.
    Yin, X.C., Pei, W.Y., Zuang, J., Hao, H.W.: Multi-orientation scene text detection with adaptive clustering. IEEE Trans. PAMI 37, 1930–1937 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • C. Naveena
    • 1
  • B. N. Ajay
    • 2
  • V. N. Manjunath Aradhya
    • 3
  1. 1.Department of CSESJBITBengaluruIndia
  2. 2.Department of CSEVTU-RRCBelgaumIndia
  3. 3.Department of MCAJSS Science and Technological UniversityMysoreIndia

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