Method for Acquisition of Camera Document Images' Distorted Gradient

Dewarping of camera document images has attracted a lot of interest over the last few years since warping not only reduces the document readability but also affects the accuracy of an OCR application. In this paper, approach for efficient getting distorted gradient of camera document images is presented. The gradient of text lines can be got by scanning black pixels’ number in text. For avoiding only getting error gradient, the image is divided into 6 zones. In each zone, a gradient is found out. Then, the dewarping can be achieved by projective transformation. Experimental results on several document images demonstrate the robustness and effectiveness of the proposed technique.

[1]  David S. Doermann,et al.  Flattening curved documents in images , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[2]  B. Gatos,et al.  Automatic Borders Detection of Camera Document Images , 2007 .

[3]  Christoph H. Lampert,et al.  Document image dewarping using robust estimation of curled text lines , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[4]  Chew Lim Tan,et al.  Correcting document image warping based on regression of curved text lines , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[5]  Zhuoqun Xu,et al.  A Page Content Independent Book Dewarping Method to Handle 2D Images Captured by a Digital Camera , 2007, ICIAR.

[6]  Faisal Shafait Document Image Dewarping Contest , 2007 .

[7]  Christoph H. Lampert,et al.  Document capture using stereo vision , 2004, DocEng '04.

[8]  W. Brent Seales,et al.  Image restoration of arbitrarily warped documents , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Seiichi Uchida,et al.  Dewarping of document image by global optimization , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[10]  Shijian Lu,et al.  The Restoration of Camera Documents Through Image Segmentation , 2006, Document Analysis Systems.

[11]  Pierre Baylou,et al.  Active contours network to straighten distorted text lines , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[12]  Gady Agam,et al.  Document Image De-warping for Text/Graphics Recognition , 2002, SSPR/SPR.

[13]  Horst Bunke,et al.  Using a Statistical Language Model to Improve the Performance of an HMM-Based Cursive Handwriting Recognition System , 2001, Int. J. Pattern Recognit. Artif. Intell..

[14]  Chew Lim Tan,et al.  Restoring Warped Document Images through 3D Shape Modeling , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Changsong Liu,et al.  A cylindrical surface model to rectify the bound document image , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[16]  Larry S. Davis,et al.  Structure of Applicable Surfaces from Single Views , 2004, ECCV.

[17]  David S. Doermann,et al.  Camera-based analysis of text and documents: a survey , 2005, International Journal of Document Analysis and Recognition (IJDAR).

[18]  Chew Lim Tan,et al.  Warped image restoration with applications to digital libraries , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[19]  Ioannis Pratikakis,et al.  Segmentation Based Recovery of Arbitrarily Warped Document Images , 2007 .

[20]  Ioannis Pratikakis,et al.  Adaptive degraded document image binarization , 2006, Pattern Recognit..

[21]  Atsushi Yamashita,et al.  Shape reconstruction and image restoration for non-flat surfaces of documents with a stereo vision system , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[22]  Michael S. Brown,et al.  Geometric and shading correction for images of printed materials: a unified approach using boundary , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..