Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Artificial Neural Networks for Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive degraded document image binarization
Pattern Recognition
Special issue on the analysis of historical documents
International Journal on Document Analysis and Recognition
International Journal on Document Analysis and Recognition
2D Euclidean distance transform algorithms: A comparative survey
ACM Computing Surveys (CSUR)
A double-threshold image binarization method based on edge detector
Pattern Recognition
Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Low quality document image modeling and enhancement
International Journal on Document Analysis and Recognition
RSLDI: Restoration of single-sided low-quality document images
Pattern Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Document Image Binarisation Using Markov Field Model
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICDAR 2009 Document Image Binarization Contest (DIBCO 2009)
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Binarization of historical document images using the local maximum and minimum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
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A recursive thresholding technique for image segmentation
IEEE Transactions on Image Processing
Multiscale morphological segmentation of gray-scale images
IEEE Transactions on Image Processing
A phase congruency based document binarization
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
A learning framework for the optimization and automation of document binarization methods
Computer Vision and Image Understanding
Historical document image restoration using multispectral imaging system
Pattern Recognition
A combined approach for the binarization of handwritten document images
Pattern Recognition Letters
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In this paper, we present an adaptive method for the binarization of historical manuscripts and degraded document images. The proposed approach is based on maximum likelihood (ML) classification and uses a priori information and the spatial relationship on the image domain. In contrast with many conventional methods that use a decision based on thresholding, the proposed method performs a soft decision based on a probabilistic model. The main idea is that, from an initialization map (under-binarization) containing only the darkest part of the text, the method is able to recover the main text in the document image, including low-intensity and weak strokes. To do so, fast and robust local estimation of text and background features is obtained using grid-based modeling and inpainting techniques; then, the ML classification is performed to classify pixels into black and white classes. The advantage of the proposed method is that it preserves weak connections and provides smooth and continuous strokes, thanks to its correlation-based nature. Performance is evaluated both subjectively and objectively against standard databases. The proposed method outperforms the state-of-the-art methods presented in the DIBCO'09 binarization contest, although those other methods provide performance close to it.