CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Restoration of Degraded Images for Text Detection and Recognition

by Sayali R. Joshi, Sankirti S. Shiravale
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134 - Number 4
Year of Publication: 2016
Authors: Sayali R. Joshi, Sankirti S. Shiravale
10.5120/ijca2016907895

Sayali R. Joshi, Sankirti S. Shiravale . Restoration of Degraded Images for Text Detection and Recognition. International Journal of Computer Applications. 134, 4 ( January 2016), 25-29. DOI=10.5120/ijca2016907895

@article{ 10.5120/ijca2016907895,
author = { Sayali R. Joshi, Sankirti S. Shiravale },
title = { Restoration of Degraded Images for Text Detection and Recognition },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 134 },
number = { 4 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume134/number4/23903-2016907895/ },
doi = { 10.5120/ijca2016907895 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:33:15.758966+05:30
%A Sayali R. Joshi
%A Sankirti S. Shiravale
%T Restoration of Degraded Images for Text Detection and Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 134
%N 4
%P 25-29
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The task of text detection natural scene images is very challenging due to the complex background and unpredictable text appearances in the image. Apart from the background and the structure of the text, unpredictability also lies in the image capturing quality. These issues include noise, orientation, low exposure, blurring, and other kinds of degradations. It is therefore necessary to first restore the target text in the image in order to ensure robust text detection and recognition. This research focuses on removing a maximum number of degradation factors from a natural scene image containing text such that the detection and recognition of the text present in that image becomes very easy. Text Specific Dictionaries will be used in order to restore the text in the images. The sparse representation method is selected with an aim to apply techniques such as denoising, deblurring, sharpening and implementing other forms of enhancement in a single text image restoration system.

References
  1. Jing Han, Jiang Yue, Yi Zhang, and Lianfa Bai, “Local Sparse Structure Denoising for Low-Light-Level Image”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 12, DECEMBER 2015
  2. Jian Sun, and Zongben Xu, “Color Image Denoising via Discriminatively Learned Iterative Shrinkage”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 11, NOVEMBER 2015
  3. Ke Gu, Guangtao Zhai, Xiaokang Yang, Wenjun Zhang, and Chang Wen Chen, “Automatic Contrast Enhancement Technology With Saliency Preservation”, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 25, NO. 9, SEPTEMBER 2015
  4. Enming Luo, Stanley H. Chan and Truong Q. Nguyen, “Adaptive Image Denoising by Targeted Databases”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 7, JULY 2015
  5. Huanjing Yue, Xiaoyan Sun, Jingyu Yang and Feng Wu, “Image Denoising by Exploring External and Internal Correlations”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 6, JUNE 2015
  6. V. Krithika, “Degradation Removal using Group Sparsity”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 3, Special Issue-1, May 2015
  7. Xiaochun Cao, Wenqi Ren, Wangmeng Zuo, Xiaojie Guo, and Hassan Foroosh, “Scene Text Deblurring Using Text-Specific Multiscale Dictionaries”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 4, APRIL 2015
  8. Ningappa T Pujar, P. B. Madhavanavar, Ramesh M.Badiger, Manjunath Kammar, “Text Extraction from Road Display Boards Using Wavelet and SVM”, International Journal of Recent Advances in Science & Engineering Volume 1, Issue 1, March, 2015
  9. Shivananda V. Seeri, J. D. Pujari and P. S. Hiremath, “Multilingual Text Localization in Natural Scene Images using Wavelet based Edge Features and Fuzzy Classification”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Volume 4, Issue 1, January-February 2015
  10. Xiaoyong Shen, Qiong Yan, Li Xu, Lizhuang Ma, Jiaya Jia,“Multispectral Joint Image Restoration via Optimizing a Scale Map”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2015
  11. Xianming Liu, Gene Cheung, Xiaolin Wu, “JOINT DENOISING AND CONTRAST ENHANCEMENT OF IMAGES USING GRAPH LAPLACIAN OPERATOR”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
  12. Mayank Tiwari and Bhupendra Gupta, “Image Denoising using Spatial Gradient Based Bilateral Filter and Minimum Mean Square Error Filtering”, Eleventh International Multi-Conference on Information Processing-2015 (IMCIP-2015)
  13. Jing Jin, Songyuan Tang, Yi Shen, “An Innovative Image Enhancement Method for Edge Preservation inWavelet Domain”,2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
  14. Guangcan Liu, Shiyu Chang, and Yi Ma,“Blind Image Deblurring Using Spectral Properties of Convolution Operators”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 12, DECEMBER 2014
  15. Rim Walha, Fadoua Drira, Adel M. Alimi, Frank Lebourgeois and Christophe Garcia,“A Sparse Coding based Approach for the Resolution Enhancement and Restoration of Printed and Handwritten Textual Images”, 14th International Conference on Frontiers in Handwriting Recognition, 2014
  16. Xin Yu, Feng Xu, Shunli Zhang, and Li Zhang, “Efficient Patch-Wise Non-Uniform Deblurring for a Single Image”. IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 16, NO. 6, OCTOBER 2014
  17. Fang Li and Tieyong Zeng, “A Universal Variational Framework for Sparsity-Based Image Inpainting”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 10, OCTOBER 2014
  18. Dong Zhang, Da-Han Wang, and Hanzi Wang, “SCENE TEXT RECOGNITION USING SPARSE CODING BASED FEATURES, IEEE International Conference on Image Processing (ICIP), 2014
  19. Enming Luo, Stanley H. Chan, and Truong Q. Nguyen, “IMAGE DENOISING BY TARGETED EXTERNAL DATABASES”, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP),2014
  20. Wangmeng Zuo, Lei Zhang, Chunwei Song, David Zhang, and Huijun Gao, “Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 6, JUNE 2014
  21. C. Paramanand and A. N. Rajagopalan, “Non-uniform Motion Deblurring for Bilayer Scenes”, IEEE Conference on Computer Vision and Pattern Recognition, 2013
  22. Florent Couzinie-Devy, Jian Sun, Karteek Alahari, Jean Ponce, “Learning to Estimate and Remove Non-uniform Image Blur”, IEEE Conference on Computer Vision and Pattern Recognition, 2013
  23. Tae Hyun Kim, Byeongjoo Ahn, and Kyoung Mu Lee, “Dynamic Scene Deblurring”, IEEE International Conference on Computer Vision, 2013
  24. Y Shi, Q Chang, X Yang, “A Robust and Fast Combination Algorithm for Deblurring and Denoising”, Signal, Image and Video Processing - Springer, June 2013
  25. Purkait, P., Chanda, B., “A FAST AND ROBUST DEBLURRING TECHNIQUE ON HIGH NOISE ENVIRONMENT”, 20th IEEE International Conference on Image Processing (ICIP), 2013
  26. Kumar, V., Bansal, A., Tulsiyan, G.H., Mishra, A., Namboodiri, A., Jawahar, C.V., “Sparse Document Image Coding for Restoration”,12th International Conference on Document Analysis and Recognition (ICDAR), 2013
  27. Walha, R., Drira, F., Lebourgeois, F., Garcia, C., Alimi, A.M., “Multiple Learned Dictionaries based Clustered Sparse Coding for the Super-Resolution of Single Text Image”, 12th International Conference on Document Analysis and Recognition (ICDAR), 2013
  28. Thanh Ha Do, Salvatore Tabbone, Oriol Ramos Terrades, “Text/graphic separation using a sparse representation with multi-learned dictionaries”, 21st International Conference on Pattern Recognition (ICPR), 2012
  29. Whyte, O., Sivic, J., Zisserman, A., Ponce, J., “Non-uniform Deblurring for Shaken Images”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010
  30. Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A., “Non-local Sparse Models for Image Restoration”, IEEE 12th International Conference on Computer Vision, 2009
  31. Huy Phat Le, Gueesang Lee , “Noise Removal from Binarized Text Images”, The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010
  32. J Mairal, G Sapiro, M Elad, “LEARNING MULTISCALE SPARSE REPRESENTATIONS FOR IMAGE AND VIDEO RESTORATION”, Multiscale Modeling & Simulation, 2008 -SIAM
  33. Celine Thillou, Silvio Ferreira, Bernard Gosselin, “An Embedded Application for Degraded Text Recognition”, EURASIP Journal on Applied Signal Processing 2005:13, 2127-2135
  34. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 3rd ed, Pearson Education, 2008
Index Terms

Computer Science
Information Sciences

Keywords

Text- Specific Dictionary Natural Scene Dictionary Image restoration Image enhancement Text Detection Text recognition Sparse representations Dictionaries.

Powered by PhDFocusTM