Pattern Recognition
A Spatial Thresholding Method for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new method for image segmentation
Computer Vision, Graphics, and Image Processing
Segmentation of Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of binary character/graphics images from grayscale document images
CVGIP: Graphical Models and Image Processing
A new approach for multilevel threshold selection
CVGIP: Graphical Models and Image Processing
Evaluation of Binarization Methods for Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improvement of “integrated function algorithm” for binarization of document images
Pattern Recognition Letters
Gray-level reduction using local spatial features
Computer Vision and Image Understanding
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Document Binarization
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive degraded document image binarization
Pattern Recognition
IBM Journal of Research and Development
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the International Workshop on Multilingual OCR
Recognition driven page orientation detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Shape based local thresholding for binarization of document images
Pattern Recognition Letters
A learning framework for the optimization and automation of document binarization methods
Computer Vision and Image Understanding
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Most of the existing document-binarization techniques deal with many parameters that require a priori setting of their values. Due to the unknown of the ground-truth images, the evaluation of document binarization techniques is subjective and employs human observers for the estimation of the appropriate parameter values. The selection of the appropriate values for these parameters is crucial and influences to the final binarization. However, there is no predetermined set of parameters that guarantees optimal binarization for all document images. This paper proposes a new technique that allows the estimation of proper parameters values for each one of the document binarization techniques. The proposed approach is based on a statistical performance analysis of a set of binarization results, which are obtained by applying various binarization techniques with different parameter values. The proposed statistical performance analysis can also depicts the best document binarization result obtained by a set of document binarization techniques.