We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Wavelet based Method for Text Segmentation in Color Images

by Priya. M, C. K. Gobu
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 3
Year of Publication: 2013
Authors: Priya. M, C. K. Gobu
10.5120/11821-7506

Priya. M, C. K. Gobu . A Wavelet based Method for Text Segmentation in Color Images. International Journal of Computer Applications. 69, 3 ( May 2013), 14-17. DOI=10.5120/11821-7506

@article{ 10.5120/11821-7506,
author = { Priya. M, C. K. Gobu },
title = { A Wavelet based Method for Text Segmentation in Color Images },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 3 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 14-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number3/11821-7506/ },
doi = { 10.5120/11821-7506 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:29:13.561451+05:30
%A Priya. M
%A C. K. Gobu
%T A Wavelet based Method for Text Segmentation in Color Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 3
%P 14-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This is a digital era, in which imaging has become simple and easy with so many handheld compact devices. Extraction of the text from captured images also becomes necessary for efficient indexing and retrieval purpose. But real time images have issues such as sensor noise, blur, viewing angle, different font size, low contrast etc. Though so many efficient methods exist, they faultier when it comes to complex background images. A wavelet based method is used here. This method preprocesses the image before it is given to an OCR filter. A color quantizer is used to minimize the number of distinct colors in the input image. A discrete wavelet transform is performed on the color quantized image which classifies the image into text and non-text pixels based on their color and the standard deviation of the wavelet. This step is followed by fuzzy C means clustering which partitions the image into the background and text regions. This preprocessed image is then passed through an OCR filter to check the quality of text being segmented.

References
  1. S. Antani, D. Crandall, R. Kasturi 2000. Robust extraction of text in video. Proceedings of the International Conference on Pattern Recognition ??ICPR' 00? ??????
  2. Bezdek, James C. 1981. Pattern Recognition with Fuzzy Objective Algorithms ISBN 0-306-40671-3.
  3. Chen X, Yuille A, 2004. Detecting and reading text in natural scenes. Proceedings of 2004 IEEE Computer Society Conference On Computer Vision And Pattern Recognition.
  4. A. Jain, B. Yu, 1998. Automatic Text Location in Images and Video Frames. Pattern Recognition 31(12):2055-2076
  5. M. Kamel, A. Zhoa, 1993. Extraction of Binary Characters/ Graphic images from gray scale document images. CVGIP: Graphics model image processing , Vol. 55, no. 3.
  6. H. K Kim,1996. Efficient automatic text location method and content-based indexing and structuring of the video database. J vi's Commun Image Represent 7 (4): 336-344
  7. F. LeBourgeois,1997. Robust multifront OCR system from Gray Level Images. International Conference on Document Analysis and Recognition, volume 1.
  8. C. M. Lee and Kankanhalli 1995. Automatic extraction of characters in Complex Scene Images. International Journal of Pattern Recognition and Artificial Intelligence, 9(1):67-82.
  9. R. Lienhart, A. Wernicke, 2002. Localizing and Segmenting Text in Images and Videos. IEEE Transcations On Circuits And Systems For Video Technology, Vol. 12,NO. 4.
  10. Y. Liu, S. Goto, T. Ikenaga, 2006. A Contour-Based Robust Algorithm for Text Detection in Color Images. IEICE TRANS. INF. & SYST. , VOL. E89-D, NO. 3.
  11. S. Messelodi and C. Modena 1999. Automatic Identification and Skew Estimation of Text Lines in Real Scene Images. Pattern Recognition, 32(5):791-810.
  12. R. Milewski and V. Govindaraju 2006. Extraction of Handwritten Text from Carbon Copy Medical Form Images. Proceedings of Seventh International Workshop Document Analysis Systems.
  13. W. Niblack 1986. An introduction to Digital Image Processing. Prentice Hall.
  14. Y. F. Pan, Xinwen. H, Cheng Lin L 2009. Text Localization in Natural Scene Images based on Conditional Random Field.
  15. J. Sauvola, T. Seppanan, S. Haapakoski, M. Pietiktinen. Adaptive Document Binarization. Proceedings of Fourth International Conference on Document Analysis and Recognition
  16. K. Sobottka, H. Bunke, H. Kronenberg 1999. Identification of Text on Colored Book and Journal Covers ICDAR.
  17. Wolf. C, Jolion J. M, Chassaing. F. Text Localization, Enhancement and Binarization in Multimedia Documents. Proceedings of International Conference on Pattern Recognition Vol. 4. ,Canada.
  18. Wu. X. 1996. YIQ Vector Quantization in a New Color Palette Architecture. IEEE Transcations on Image Processing, Vol. 5, No. 2. 321-329
  19. Q. Ye, Q. Huang, W. Gao,D. Zhoa 2005. Fast and Robust Text Detection in Images and Video Frames. Image and Vision Computing 23. 565-576
  20. D. Q. Zhang 2004. Learning to detect scene text using a higher-order MRF with Belief Propagation 27-02.
Index Terms

Computer Science
Information Sciences

Keywords

Discrete Wavelet Transform Fuzzy C-Means Clustering OCR Segmentation

Powered by PhDFocusTM