An introduction to digital image processing
An introduction to digital image processing
Adaptive Document Binarization
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A binarization algorithm for historical manuscripts
ICCOM'08 Proceedings of the 12th WSEAS international conference on Communications
Document Image Binarisation Using Markov Field Model
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
DIBCO 2009: document image binarization contest
International Journal on Document Analysis and Recognition - Special Issue on Performance Evaluation
Adaptive thresholding methods for documents image binarization
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Adaptive binarization method for enhancing ancient malay manuscript images
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
A new binarization method for non-uniform illuminated document images
Pattern Recognition
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Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.