Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
ICDAR 2009 Document Image Binarization Contest (DIBCO 2009)
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
A multi-scale framework for adaptive binarization of degraded document images
Pattern Recognition
Binarization of historical document images using the local maximum and minimum
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Document image binarization using background estimation and stroke edges
International Journal on Document Analysis and Recognition
A Self-Training Learning Document Binarization Framework
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
H-DIBCO 2010 - Handwritten Document Image Binarization Competition
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
ICDAR 2011 Document Image Binarization Contest (DIBCO 2011)
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Combination of Document Image Binarization Techniques
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
New Binarization Approach Based on Text Block Extraction
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
A phase congruency based document binarization
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
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In this paper, phase congruency features are used to develop a binarization method for degraded documents and manuscripts. Also, Gaussian and median filtering are used in order to improve the final binarized output. Gaussian filter is used for further enhance the output and median filter is applied to remove noises. To detect bleed-through degradation, a feature map based on regional minima is proposed and used. The proposed binarization method provides output binary images with high recall values and competitive precision values. Promising experimental results obtained on the DIBCO'09, H-DIBCO'10 and DIBCO'11 datasets, and this shows the robustness of the proposed binarization method against a large number of different types of degradation.