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

• 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
• Jian Sun, and Zongben Xu, “Color Image Denoising via Discriminatively Learned Iterative Shrinkage”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 11, NOVEMBER 2015
• 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
• 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
• 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
• V. Krithika, “Degradation Removal using Group Sparsity”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 3, Special Issue-1, May 2015
• 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
• 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
• 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
• 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
• 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
• 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)
• 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)
• 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
• 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
• 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
• 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
• Dong Zhang, Da-Han Wang, and Hanzi Wang, “SCENE TEXT RECOGNITION USING SPARSE CODING BASED FEATURES, IEEE International Conference on Image Processing (ICIP), 2014
• 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
• 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
• C. Paramanand and A. N. Rajagopalan, “Non-uniform Motion Deblurring for Bilayer Scenes”, IEEE Conference on Computer Vision and Pattern Recognition, 2013
• 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
• Tae Hyun Kim, Byeongjoo Ahn, and Kyoung Mu Lee, “Dynamic Scene Deblurring”, IEEE International Conference on Computer Vision, 2013
• Y Shi, Q Chang, X Yang, “A Robust and Fast Combination Algorithm for Deblurring and Denoising”, Signal, Image and Video Processing - Springer, June 2013
• Purkait, P., Chanda, B., “A FAST AND ROBUST DEBLURRING TECHNIQUE ON HIGH NOISE ENVIRONMENT”, 20th IEEE International Conference on Image Processing (ICIP), 2013
• 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
• 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
• 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
• Whyte, O., Sivic, J., Zisserman, A., Ponce, J., “Non-uniform Deblurring for Shaken Images”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010
• 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
• Huy Phat Le, Gueesang Lee , “Noise Removal from Binarized Text Images”, The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010
• J Mairal, G Sapiro, M Elad, “LEARNING MULTISCALE SPARSE REPRESENTATIONS FOR IMAGE AND VIDEO RESTORATION”, Multiscale Modeling & Simulation, 2008 -SIAM
• Celine Thillou, Silvio Ferreira, Bernard Gosselin, “An Embedded Application for Degraded Text Recognition”, EURASIP Journal on Applied Signal Processing 2005:13, 2127-2135
• Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 3rd ed, Pearson Education, 2008