Restoration based Contourlet Transform for historical document image binarization

We propose in this paper, the use of the Contourlet Transform for evaluating the quality of the degraded historical document. To facilitate the binarization, we first improve the quality of the document image by applying the Contourlet Transform, in order to select significant coefficients. After reconstruction, a local thresholding method is used for extracting the foreground text. The proposed method is evaluated on the benchmarking dataset used in the international document image binarization contest (DIBCO 2009/2011 and H-DIBCO 2010/2012) according to the type of degradation. Promising results are obtained comparatively to the standard methods.

[1]  Matti Pietikäinen,et al.  Adaptive document image binarization , 2000, Pattern Recognit..

[2]  T. W. Ridler,et al.  Picture thresholding using an iterative selection method. , 1978 .

[3]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[4]  Ioannis Pratikakis,et al.  H-DIBCO 2010 - Handwritten Document Image Binarization Competition , 2010, 2010 12th International Conference on Frontiers in Handwriting Recognition.

[5]  Youcef Chibani,et al.  Ridgelet-DTW-based word spotting for Arabic historical document , 2013, 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA).

[6]  Youcef Chibani,et al.  Enhancement of Historical Document Images by Combining Global and Local Binarization Technique , 2014 .

[7]  Wayne Nilback An introduction to digital image processing , 1985 .

[8]  B. Kapralos,et al.  I An Introduction to Digital Image Processing , 2022 .

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

[10]  Ioannis Pratikakis,et al.  ICDAR 2011 Document Image Binarization Contest (DIBCO 2011) , 2011, 2011 International Conference on Document Analysis and Recognition.

[11]  Ioannis Pratikakis,et al.  ICDAR 2009 Document Image Binarization Contest (DIBCO 2009) , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[12]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[13]  Youcef Chibani,et al.  Machine printed handwritten text discrimination using Radon transform and SVM classifier , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[14]  Ioannis Pratikakis,et al.  ICFHR 2012 Competition on Handwritten Document Image Binarization (H-DIBCO 2012) , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.