Evaluation of Binarization Methods for Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Robust Real-Time Face Detection
International Journal of Computer Vision
Adaptive degraded document image binarization
Pattern Recognition
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
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
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Historical document binarization based on phase information of images
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
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In this paper, three new methods proposed for binarization of degraded documents and manuscripts. Phase congruency used to select regions of interest (ROI) of document's foreground. The main idea is to achieve an optimal recall measure (recall˜1), while the precision value is at an acceptable level. Further processing should be performed to focus on the ROI. We also used a modified adaptive thresholding method in the next step. This method uses a global variance, a global mean and local means for thresholding. Finally, a new method called early exclusion criterion (EEC) proposed for document enhancement. The experimental results on the datasets introduced in DIBCO 2009, H-DIBCO 2010 and DIBCO 2011 shows that near optimal recall value (recall˜0.99) obtained, while precision measure's value is acceptable.