Adaptive fuzzy model for blur estimation on document images

A new local approach based on a clustering of blur and non-blur classes to deal with heterogeneous blur in document images.Non linear approach.A new blur feature for clustering blur and non-blur classes.The study on the impact of blur on OCR accuracy.The first comparison on standard databases. In this paper, we propose a local blur estimation for document images captured by portable cameras. A novel blur pixel feature is extracted from pixels properties in working zones to initialize a fuzzy clustering of blur and non-blur classes. At the final state of the process, a blur region is determined for each working zone. The blur score is given by the average of all membership values of pixels in the blur region. The quantitative evaluation on two real databases (DIQA and an industrial database) shows that our method achieves good results in comparison with recent methods estimated on these databases.

[1]  Beatriz Marcotegui,et al.  Local Blur Estimation Based on Toggle Mapping , 2015, ISMM.

[2]  Henry S. Baird,et al.  Document image defect models , 1995 .

[3]  Henry P. Kramer,et al.  Iterations of a non-linear transformation for enhancement of digital images , 1975, Pattern Recognit..

[4]  Lionel Moisan,et al.  No-Reference Image Quality Assessment and Blind Deblurring with Sharpness Metrics Exploiting Fourier Phase Information , 2015, Journal of Mathematical Imaging and Vision.

[5]  Alan C. Bovik,et al.  No-reference image blur assessment using multiscale gradient , 2009, 2009 International Workshop on Quality of Multimedia Experience.

[6]  Nicole Vincent,et al.  Local blur correction for document images , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[7]  David S. Doermann,et al.  A Dataset for Quality Assessment of Camera Captured Document Images , 2013, CBDAR.

[8]  Brian C. Lovell,et al.  Face Recognition from Still Images to Video Sequences: A Local-Feature-Based Framework , 2011, EURASIP J. Image Video Process..

[9]  Wilfried Philips,et al.  Efficient blur estimation using multi-scale quadrature filters , 2013, Signal Process..

[10]  Ben Baker.  Blur Detection for Historical Document Images , 2011 .

[11]  Brian C. Lovell,et al.  A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts , 2013, EURASIP J. Image Video Process..

[12]  Zhou Wang,et al.  Reduced- and No-Reference Image Quality Assessment , 2011, IEEE Signal Processing Magazine.

[13]  Shijian Lu,et al.  Blurred image region detection and classification , 2011, ACM Multimedia.

[14]  Bart Lamiroy,et al.  Text/Graphics Separation Revisited , 2002, Document Analysis Systems.

[15]  Jiaya Jia,et al.  Image partial blur detection and classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Palaiahnakote Shivakumara,et al.  A blind deconvolution model for scene text detection and recognition in video , 2016, Pattern Recognit..

[17]  Mickaël Coustaty,et al.  ICDAR2015 competition on smartphone document capture and OCR (SmartDoc) , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[18]  Nicole Vincent,et al.  Comparison of Niblack inspired binarization methods for ancient documents , 2009, Electronic Imaging.

[19]  Ming-Hsuan Yang,et al.  $L_0$ -Regularized Intensity and Gradient Prior for Deblurring Text Images and Beyond , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Lina J. Karam,et al.  A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB) , 2009, IEEE Transactions on Image Processing.

[21]  David S. Doermann,et al.  Unsupervised feature learning framework for no-reference image quality assessment , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  David S. Doermann,et al.  Sharpness estimation for document and scene images , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[23]  Shijian Lu,et al.  Restoration of motion blurred document images , 2012, SAC '12.

[24]  Xujun Peng,et al.  Automated image quality assessment for camera-captured OCR , 2011, 2011 18th IEEE International Conference on Image Processing.

[25]  Seungyong Lee,et al.  Text Image Deblurring Using Text-Specific Properties , 2012, ECCV.

[26]  Zhou Wang,et al.  No-reference image sharpness assessment based on local phase coherence measurement , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[27]  Lina J. Karam,et al.  A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD) , 2011, IEEE Transactions on Image Processing.

[28]  Qiang Wu,et al.  An effective document image deblurring algorithm , 2011, CVPR 2011.

[29]  Gwendoline Blanchet,et al.  An explicit sharpness index related to global phase coherence , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[30]  D. Chandler.  Seven Challenges in Image Quality Assessment: Past, Present, and Future Research , 2013 .

[31]  Marcelo H. Ang,et al.  Practical issues in pixel-based autofocusing for machine vision , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[32]  Guangjun Zhang,et al.  A no-reference sharpness metric based on the notion of relative blur for Gaussian blurred image , 2014, J. Vis. Commun. Image Represent..

[33]  David S. Doermann,et al.  Document Image Quality Assessment: A Brief Survey , 2013, 2013 12th International Conference on Document Analysis and Recognition.