An introduction to digital image processing
An introduction to digital image processing
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
Binarization and multithresholding of document images using connectivity
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
Document Image Binarization Based on Texture Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IBM Journal of Research and Development
A binarization algorithm for historical manuscripts
ICCOM'08 Proceedings of the 12th WSEAS international conference on Communications
A new binarization method for non-uniform illuminated document images
Pattern Recognition
Hi-index | 0.00 |
This paper proposes an integrated system for the binarization of normal and degraded printed documents for the purpose of visualization and recognition of text characters. In degraded documents, where considerable background noise or variation in contrast and illumination exists, there are many pixels that cannot be easily classified as foreground or background pixels. For this reason, it is necessary to perform document binarization by combining and taking into account the results of a set of binarization techniques, especially for document pixels that have high vagueness. The proposed binarization technique takes advantage of the benefits of a set of selected binarization algorithms by combining their results using a Kohonen self-organizing map neural network. Specifically, in the first stage the best parameter values for each independent binarization technique are estimated. In the second stage and in order to take advantage of the binarization information given by the independent techniques, the neural network is fed by the binarization results obtained by those techniques using their estimated best parameter values. This procedure is adaptive because the estimation of the best parameter values depends on the content of images. The proposed binarization technique is extensively tested with a variety of degraded document images. Several experimental and comparative results, exhibiting the performance of the proposed technique, are presented.