Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
A tutorial on support vector regression
Statistics and Computing
Adaptive degraded document image binarization
Pattern Recognition
Special issue on the analysis of historical documents
International Journal on Document Analysis and Recognition
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
Classifying Foreground Pixels in Document Images
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Feature Based Binarization of Document Images Degraded by Uneven Light Condition
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
Estimation of proper parameter values for document binarization
CGIM '08 Proceedings of the Tenth IASTED International Conference on Computer Graphics and Imaging
A multi-scale framework for adaptive binarization of degraded document images
Pattern Recognition
An analysis of binarization ground truthing
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Binarization of historical document images using the local maximum and minimum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
A Variational Approach to Degraded Document Enhancement
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document image binarization using background estimation and stroke edges
International Journal on Document Analysis and Recognition
A clustering rule-based approach to predictive modeling
Proceedings of the 48th Annual Southeast Regional Conference
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
DIBCO 2009: document image binarization contest
International Journal on Document Analysis and Recognition - Special Issue on Performance Evaluation
A Tool for Tuning Binarization Techniques
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
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Cleaning and enhancing historical document images
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Effect of "Ground Truth" on Image Binarization
DAS '12 Proceedings of the 2012 10th IAPR International Workshop on Document Analysis Systems
Adapting the Turing Test for Declaring Document Analysis Problems Solved
DAS '12 Proceedings of the 2012 10th IAPR International Workshop on Document Analysis Systems
A ground truth bleed-through document image database
TPDL'12 Proceedings of the Second international conference on Theory and Practice of Digital Libraries
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Almost all binarization methods have a few parameters that require setting. However, they do not usually achieve their upper-bound performance unless the parameters are individually set and optimized for each input document image. In this work, a learning framework for the optimization of the binarization methods is introduced, which is designed to determine the optimal parameter values for a document image. The framework, which works with any binarization method, has a standard structure, and performs three main steps: (i) extracts features, (ii) estimates optimal parameters, and (iii) learns the relationship between features and optimal parameters. First, an approach is proposed to generate numerical feature vectors from 2D data. The statistics of various maps are extracted and then combined into a final feature vector, in a nonlinear way. The optimal behavior is learned using support vector regression (SVR). Although the framework works with any binarization method, two methods are considered as typical examples in this work: the grid-based Sauvola method, and Lu's method, which placed first in the DIBCO'09 contest. The experiments are performed on the DIBCO'09 and H-DIBCO'10 datasets, and combinations of these datasets with promising results.