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Reseach Article

Crossbreed Thresholding Text extraction Procedure for Images using DWT Domain and SVM Classifier

by Manisha Bansal, Naresh Kumar Garg
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134 - Number 5
Year of Publication: 2016
Authors: Manisha Bansal, Naresh Kumar Garg
10.5120/ijca2016907957

Manisha Bansal, Naresh Kumar Garg . Crossbreed Thresholding Text extraction Procedure for Images using DWT Domain and SVM Classifier. International Journal of Computer Applications. 134, 5 ( January 2016), 23-28. DOI=10.5120/ijca2016907957

@article{ 10.5120/ijca2016907957,
author = { Manisha Bansal, Naresh Kumar Garg },
title = { Crossbreed Thresholding Text extraction Procedure for Images using DWT Domain and SVM Classifier },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 134 },
number = { 5 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 23-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume134/number5/23911-2016907957/ },
doi = { 10.5120/ijca2016907957 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:33:21.437193+05:30
%A Manisha Bansal
%A Naresh Kumar Garg
%T Crossbreed Thresholding Text extraction Procedure for Images using DWT Domain and SVM Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 134
%N 5
%P 23-28
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a novel crossbreed technique to vigorously extract the texts in images based on Discreet Wavelet Transform (DWT) and Support Vector Machine (SVM). Images on which experimentation has been done are taken from various book covers, newspapers, magazines and commercial products. Database of proposed technique includes 25 images. In addition to that the proposed technique is robust to language selection of the text that is embedded in an image. Experimental database includes images that contain English, Punjabi as well as Hindi font. The proposed technique can be used in the applications such as; keyword-based searching, document retrieving, database collection in an organized manner etc. The projected work is estimated using ICDAR 2013 competition metrics specification and the performance is good as well as results are promising for 3 languages as well.

References
  1. Uddin, Sultana, M. Rahman, Busra, “Extraction of text from Scene Image using Morphological based approach”, International Conference on Machine and Vision, IEEE, pp.876-880 (2012).
  2. Matko Saric, Hrvoje Dujmic, Mladen Russo, “scene text extraction in HIS color space using K-means and modified cylindrical distance”, PRZEGLĄD ELEKTROTECHNICZNY, pp. 117-121 (2013).
  3. C. A. Bouman: Digital Image Processing - January 13 (2014).
  4. Fatma H. Elfouly, Mohamed I. Mahmoud, Moawad I. M. Dessouky, and Salah Deyab “Comparison between Daubechies wavelet and Haar transform using FGPA”, World Academy of Science,Engineering and Technology, pp.395-400 (2008).
  5. Punam Patel, Shamik Tiwari, “Text segmentation From Images”, International Journal of Computer Applications, pp.25-28 (2013).
  6. Neha Gupta, V.K Banga , “Localization of Text in Complex Images Using Haar Wavelet Transform”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), pp.111-115 (2012).
  7. A.J.Jadhav Vaibhav Kolhe Sagar Peshwe, “Text Extraction from Images: A Survey”, IJARCSSE, pp.333-337 (2013).
  8. R. Chandrasekaran, RM. Chandrasekaran, “Morphological Text Extraction in Images”, IJSCT, vol-2, pp.103-107 (2011).
  9. R. Chandrasekaran, RM. Chandrasekaran, P Natrajan, “Text Localization and Extraction in Images Using Mathematical Morphology and SVM”, International Conference on Computer Pattern and Recognition, IEEE, pp.55-60 (2012).
  10. Anubhav Kumar, “An Efficient Text Extraction Algorithm in Images”, Conference on Contemporary Computing, IEEE, pp.6-12 (2013).
  11. B.H. Shekhar, Smitha M.L, P. Shivkumara, “Discrete Wavelet Transform and Gradient Difference based approach for text localization in videos”, Fifth International Conference on Signals and Image Processing, IEEE, pp.280-284 (2013).
  12. Fixing Ye, Qingming Huang, Wen Gao and Debin Zhao, “Fast and Robust text detection in images and video frames”, Image and Vision Computing 23 (2005).
  13. C. A. Bouman "Connected Component Analysis," Digital Image Processing, pp. 1-19, January 10 (2011).
  14. R.C. Gonzales and R.E. Woods, Digital Image Processing, Addison-Wesley, Reading (1992).
  15. Leon, M., Vilaplana, V., Gasull, A. and Marques, F., "Caption text extraction for indexing purposes using a hierarchical region-based image model”, International Conference on Image Processing, IEEE, El Cairo, Egypt (2009).
  16. J.Sushma, M.Padmaja, "Text detection in colour Images", International Conference on Image Processing, IEEE (2009).
  17. S.Abirami, Dr. D.Manjula, "A Survey of Script Identification techniques for Multi-Script Document Images", International Journal of Recent Trends in Engineering, Vol. 1, No. 2 (2009).
  18. Davod Zaravi, Habib Rostami, Alireza Malahzaheh, S.S Mortazavi, "Journals Subheadlines Text Extraction UsingWavelet Thresholding and New Projection Profile World",World Academy of Science, Engineering and Technology (2011).
  19. J.Fabrizio, M. Cord, B. Marcotegui, "Text extraction from street level Images", CMRT09. IAPRS, Vol. XXXVIII, Part 3/ W4 3-4 (2009).
  20. C. Liu, C. Wang and R. Dai. “Text Detection in Images Based on Unsupervised Classification of Edge-based Features”, pp. 610-614, ICDAR (2005).
  21. Niti Syal, Naresh Kumar Garg, “Text Extraction in Images Using DWT, Gradient method and SVM Classifier”, IJEATE (2015).
Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machines Gradient Difference Discreet Wavelet Transform.